Manoma, kad žmogaus organizmą sudaro apie 50 mlrd. ląstelių, skirstomų į 220 tipų.
46 chromosomose yra apie 24 tūkst. genų, o baltymus koduoja apie 1.1% - 1.4% genomo. DNR saugo milijardą (109) bitų informacijos.
http://en.wikipedia.org/wiki/Cell_%28biology%29
2010 m. balandžio 30 d., penktadienis
2010 m. balandžio 28 d., trečiadienis
Molekulinė piktybinių susirgimų klasifikacija - literatūra
pagal Golub straipsnio citavimus
http://www.sciencemag.org/cgi/content/full/286/5439/531
Medicininiai 86
1
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Gene Expression Analysis Reveals a Gene Set Discriminatory to Different Metals in Soil.
B. Nota, R. A. Verweij, D. Molenaar, B. Ylstra, N. M. van Straalen, and D. Roelofs (2010)
Toxicol. Sci. 115, 34-40
| Abstract » | Full Text » | PDF »
3
Refocusing the War on Cancer: The Critical Role of Personalized Treatment.
A. Potti, R. L. Schilsky, and J. R. Nevins (2010)
Science Translational Medicine 2, 28cm13
| Full Text » | PDF »
4
A pathway-based classification of human breast cancer.
M. L. Gatza, J. E. Lucas, W. T. Barry, J. W. Kim, Q. Wang, M. D. Crawford, M. B. Datto, M. Kelley, B. Mathey-Prevot, A. Potti, et al. (2010)
PNAS 107, 6994-6999
| Abstract » | Full Text » | PDF »
7
Gene Expression-Based Classification As an Independent Predictor of Clinical Outcome in Juvenile Myelomonocytic Leukemia.
S. Bresolin, M. Zecca, C. Flotho, L. Trentin, A. Zangrando, L. Sainati, J. Stary, B. de Moerloose, H. Hasle, C. M. Niemeyer, et al. (2010)
J. Clin. Oncol. 28, 1919-1927
| Abstract » | Full Text » | PDF »
8
Leukemogenic transformation by HOXA cluster genes.
C. Bach, S. Buhl, D. Mueller, M. P. Garcia-Cuellar, E. Maethner, and R. K. Slany (2010)
Blood 115, 2910-2918
| Abstract » | Full Text » | PDF »
10
Differential Protein Expression Profiles in Estrogen Receptor-Positive and -Negative Breast Cancer Tissues Using Label-Free Quantitative Proteomics.
K. Rezaul, J. K. Thumar, D. H. Lundgren, J. K. Eng, K. P. Claffey, L. Wilson, and D. K. Han (2010)
Genes & Cancer 1, 251-271
| Abstract » | Full Text » | PDF »
11
A motif-based analysis of glycan array data to determine the specificities of glycan-binding proteins.
A. Porter, T. Yue, L. Heeringa, S. Day, E. Suh, and B. B Haab (2010)
Glycobiology 20, 369-380
| Abstract » | Full Text » | PDF »
16
Age- and Sex-Specific Genomic Profiles in Non-Small Cell Lung Cancer.
W. Mostertz, M. Stevenson, C. Acharya, I. Chan, K. Walters, W. Lamlertthon, W. Barry, J. Crawford, J. Nevins, and A. Potti (2010)
JAMA 303, 535-543
| Abstract » | Full Text » | PDF »
18
Quantitative Proteomic Profiling of Prostate Cancer Reveals a Role for miR-128 in Prostate Cancer.
A. P. Khan, L. M. Poisson, V. B. Bhat, D. Fermin, R. Zhao, S. Kalyana-Sundaram, G. Michailidis, A. I. Nesvizhskii, G. S. Omenn, A. M. Chinnaiyan, et al. (2010)
Mol. Cell. Proteomics 9, 298-312
| Abstract » | Full Text » | PDF »
22
Reversing HOXA9 Oncogene Activation by PI3K Inhibition: Epigenetic Mechanism and Prognostic Significance in Human Glioblastoma.
B. M. Costa, J. S. Smith, Y. Chen, J. Chen, H. S. Phillips, K. D. Aldape, G. Zardo, J. Nigro, C. D. James, J. Fridlyand, et al. (2010)
Cancer Res. 70, 453-462
| Abstract » | Full Text » | PDF »
27
Characterizing the Clinical Relevance of an Embryonic Stem Cell Phenotype in Lung Adenocarcinoma.
M. Stevenson, W. Mostertz, C. Acharya, W. Kim, K. Walters, W. Barry, K. Higgins, S. A. Tuchman, J. Crawford, G. Vlahovic, et al. (2009)
Clin. Cancer Res. 15, 7553-7561
| Abstract » | Full Text » | PDF »
29
Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival.
D. Bogunovic, D. W. O'Neill, I. Belitskaya-Levy, V. Vacic, Y.-L. Yu, S. Adams, F. Darvishian, R. Berman, R. Shapiro, A. C. Pavlick, et al. (2009)
PNAS 106, 20429-20434
| Abstract » | Full Text » | PDF »
30
Global Transcriptional Response to Spermine, a Component of the Intramacrophage Environment, Reveals Regulation of Francisella Gene Expression through Insertion Sequence Elements.
P. E. Carlson Jr., J. Horzempa, D. M. O'Dee, C. M. Robinson, P. Neophytou, A. Labrinidis, and G. J. Nau (2009)
J. Bacteriol. 191, 6855-6864
| Abstract » | Full Text » | PDF »
32
Modeling the functional heterogeneity of leukemia stem cells: role of STAT5 in leukemia stem cell self-renewal.
M. Heuser, L. M. Sly, B. Argiropoulos, F. Kuchenbauer, C. Lai, A. Weng, M. Leung, G. Lin, C. Brookes, S. Fung, et al. (2009)
Blood 114, 3983-3993
| Abstract » | Full Text » | PDF »
33
ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing.
G. Quon and Q. Morris (2009)
Bioinformatics 25, 2882-2889
| Abstract » | Full Text » | PDF »
34
Genoproteomic Mining of Urothelial Cancer Suggests {gamma}-Glutamyl Hydrolase and Diazepam-Binding Inhibitor as Putative Urinary Markers of Outcome after Chemotherapy.
C. Pollard, M. Nitz, A. Baras, P. Williams, C. Moskaluk, and D. Theodorescu (2009)
Am. J. Pathol. 175, 1824-1830
| Abstract » | Full Text » | PDF »
35
The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data.
V. G. Oehler, K. Y. Yeung, Y. E. Choi, R. E. Bumgarner, A. E. Raftery, and J. P. Radich (2009)
Blood 114, 3292-3298
| Abstract » | Full Text » | PDF »
36
Variable selection and dependency networks for genomewide data.
A. Dobra (2009)
Biostat. 10, 621-639
| Abstract » | Full Text » | PDF »
37
HOXA9 Modulates Its Oncogenic Partner Meis1 To Influence Normal Hematopoiesis.
Y.-L. Hu, S. Fong, C. Ferrell, C. Largman, and W.-F. Shen (2009)
Mol. Cell. Biol. 29, 5181-5192
| Abstract » | Full Text » | PDF »
38
New Molecular Classifications of Breast Cancer.
M. Cianfrocca and W. Gradishar (2009)
CA Cancer J Clin 59, 303-313
| Abstract » | Full Text » | PDF »
39
Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation.
J. Tung, O. Fedrigo, R. Haygood, S. Mukherjee, and G. A. Wray (2009)
Mol. Biol. Evol. 26, 2047-2059
| Abstract » | Full Text » | PDF »
40
Organization of the autoantibody repertoire in healthy newborns and adults revealed by system level informatics of antigen microarray data.
A. Madi, I. Hecht, S. Bransburg-Zabary, Y. Merbl, A. Pick, M. Zucker-Toledano, F. J. Quintana, A. I. Tauber, I. R. Cohen, and E. Ben-Jacob (2009)
PNAS 106, 14484-14489
| Abstract » | Full Text » | PDF »
41
High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications.
S. Ocak, M. L. Sos, R. K. Thomas, and P. P. Massion (2009)
Eur. Respir. J. 34, 489-506
| Abstract » | Full Text » | PDF »
42
Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging.
G. Alexe, J. Monaco, S. Doyle, A. Basavanhally, A. Reddy, M. Seiler, S. Ganesan, G. Bhanot, and A. Madabhushi (2009)
Exp Biol Med 234, 860-879
| Abstract » | Full Text » | PDF »
44
Molecular Imaging of the Breast.
R. L. Birdwell, C. E. Mountford, and J. D. Iglehart (2009)
Am. J. Roentgenol. 193, 367-376
| Abstract » | Full Text » | PDF »
45
Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome.
K. I. Mills, A. Kohlmann, P. M. Williams, L. Wieczorek, W.-m. Liu, R. Li, W. Wei, D. T. Bowen, H. Loeffler, J. M. Hernandez, et al. (2009)
Blood 114, 1063-1072
| Abstract » | Full Text » | PDF »
46
A Network Model of a Cooperative Genetic Landscape in Brain Tumors.
M. Bredel, D. M. Scholtens, G. R. Harsh, C. Bredel, J. P. Chandler, J. J. Renfrow, A. K. Yadav, H. Vogel, A. C. Scheck, R. Tibshirani, et al. (2009)
JAMA 302, 261-275
| Abstract » | Full Text » | PDF »
47
Transcriptional profiling enables molecular classification of adrenocortical tumours.
C. Laurell, D. Velazquez-Fernandez, K. Lindsten, C. Juhlin, U. Enberg, J. Geli, A. Hoog, M. Kjellman, J. Lundeberg, B. Hamberger, et al. (2009)
Eur. J. Endocrinol. 161, 141-152
| Abstract » | Full Text » | PDF »
48
Comparative Analysis of the Membrane Proteome of Closely Related Metastatic and Nonmetastatic Tumor Cells.
C. Roesli, B. Borgia, C. Schliemann, M. Gunthert, H. Wunderli-Allenspach, R. Giavazzi, and D. Neri (2009)
Cancer Res. 69, 5406-5414
| Abstract » | Full Text » | PDF »
49
Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction.
C. Cheng, K. Shen, C. Song, J. Luo, and G. C. Tseng (2009)
Bioinformatics 25, 1655-1661
| Abstract » | Full Text » | PDF »
50
Identification of AML1-ETO modulators by chemical genomics.
S. M. Corsello, G. Roti, K. N. Ross, K. T. Chow, I. Galinsky, D. J. DeAngelo, R. M. Stone, A. L. Kung, T. R. Golub, and K. Stegmaier (2009)
Blood 113, 6193-6205
| Abstract » | Full Text » | PDF »
51
Efficacy and pharmacodynamic effects of bosutinib (SKI-606), a Src/Abl inhibitor, in freshly generated human pancreas cancer xenografts.
W. A. Messersmith, N.V. Rajeshkumar, A. C. Tan, X. F. Wang, V. Diesl, S. E. Choe, M. Follettie, C. Coughlin, F. Boschelli, E. Garcia-Garcia, et al. (2009)
Mol. Cancer Ther. 8, 1484-1493
| Abstract » | Full Text » | PDF »
52
Perspectives of gene expression profiling for diagnosis and therapy in haematological malignancies.
U. Bacher, A. Kohlmann, and T. Haferlach (2009)
Briefings in Functional Genomics
| Abstract » | Full Text » | PDF »
53
Cyclodepsipeptide toxin promotes the degradation of Hsp90 client proteins through chaperone-mediated autophagy.
S. Shen, P. Zhang, M. A. Lovchik, Y. Li, L. Tang, Z. Chen, R. Zeng, D. Ma, J. Yuan, and Q. Yu (2009)
J. Cell Biol. 185, 629-639
| Abstract » | Full Text » | PDF »
54
Uncovering multiple molecular targets for caffeine using a drug target validation strategy combining A2A receptor knockout mice with microarray profiling.
L. Yu, J. E. Coelho, X. Zhang, Y. Fu, A. Tillman, U. Karaoz, B. B. Fredholm, Z. Weng, and J.-F. Chen (2009)
Physiol Genomics 37, 199-210
| Abstract » | Full Text » | PDF »
55
Four-Gene Expression Ratio Test for Survival in Patients Undergoing Surgery for Mesothelioma.
G. J. Gordon, L. Dong, B. Y. Yeap, W. G. Richards, J. N. Glickman, H. Edenfield, M. Mani, R. Colquitt, G. Maulik, B. Van Oss, et al. (2009)
J Natl Cancer Inst 101, 678-686
| Abstract » | Full Text » | PDF »
56
A roadmap of clustering algorithms: finding a match for a biomedical application.
B. Andreopoulos, A. An, X. Wang, and M. Schroeder (2009)
Brief Bioinform 10, 297-314
| Abstract » | Full Text » | PDF »
57
Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips.
Y.-S. Lee, C.-H. Chen, C.-N. Tsai, C.-L. Tsai, A. Chao, and T.-H. Wang (2009)
Nucleic Acids Res. 37, e61
| Abstract » | Full Text » | PDF »
58
MIST: Maximum Information Spanning Trees for dimension reduction of biological data sets.
B. M. King and B. Tidor (2009)
Bioinformatics 25, 1165-1172
| Abstract » | Full Text » | PDF »
59
Global Levels of Histone Modifications Predict Prognosis in Different Cancers.
D. B. Seligson, S. Horvath, M. A. McBrian, V. Mah, H. Yu, S. Tze, Q. Wang, D. Chia, L. Goodglick, and S. K. Kurdistani (2009)
Am. J. Pathol. 174, 1619-1628
| Abstract » | Full Text » | PDF »
60
Regulation of mir-196b by MLL and its overexpression by MLL fusions contributes to immortalization.
R. Popovic, L. E. Riesbeck, C. S. Velu, A. Chaubey, J. Zhang, N. J. Achille, F. E. Erfurth, K. Eaton, J. Lu, H. L. Grimes, et al. (2009)
Blood 113, 3314-3322
| Abstract » | Full Text » | PDF »
61
Tissue Biomarkers for Prognosis in Cutaneous Melanoma: A Systematic Review and Meta-analysis.
B. E. G. Rothberg, M. B. Bracken, and D. L. Rimm (2009)
J Natl Cancer Inst 101, 452-474
| Abstract » | Full Text » | PDF »
62
Identification of differential gene pathways with principal component analysis.
S. Ma and M. R. Kosorok (2009)
Bioinformatics 25, 882-889
| Abstract » | Full Text » | PDF »
63
Utilizing the Molecular Gateway: The Path to Personalized Cancer Management.
J. B. Overdevest, D. Theodorescu, and J. K. Lee (2009)
Clin. Chem. 55, 684-697
| Abstract » | Full Text » | PDF »
64
Characterizing the developmental pathways TTF-1, NKX2-8, and PAX9 in lung cancer.
D. S. Hsu, C. R. Acharya, B. S. Balakumaran, R. F. Riedel, M. K. Kim, M. Stevenson, S. Tuchman, S. Mukherjee, W. Barry, H. K. Dressman, et al. (2009)
PNAS 106, 5312-5317
| Abstract » | Full Text » | PDF »
65
Molecular profiling of classical Hodgkin lymphoma tissues uncovers variations in the tumor microenvironment and correlations with EBV infection and outcome.
B. Chetaille, F. Bertucci, P. Finetti, B. Esterni, A. Stamatoullas, J. M. Picquenot, M. C. Copin, F. Morschhauser, O. Casasnovas, T. Petrella, et al. (2009)
Blood 113, 2765-3775
| Abstract » | Full Text » | PDF »
66
Drug Discovery in Psychiatric Illness: Mining for Gold.
G. I. Elmer and N. Kafkafi (2009)
Schizophr Bull
| Abstract » | Full Text » | PDF »
67
HOXA9 is required for survival in human MLL-rearranged acute leukemias.
J. Faber, A. V. Krivtsov, M. C. Stubbs, R. Wright, T. N. Davis, M. van den Heuvel-Eibrink, C. M. Zwaan, A. L. Kung, and S. A. Armstrong (2009)
Blood 113, 2375-2385
| Abstract » | Full Text » | PDF »
68
Reciprocal Regulation of Myocardial microRNAs and Messenger RNA in Human Cardiomyopathy and Reversal of the microRNA Signature by Biomechanical Support.
S. J. Matkovich, D. J. Van Booven, K. A. Youker, G. Torre-Amione, A. Diwan, W. H. Eschenbacher, L. E. Dorn, M. A. Watson, K. B. Margulies, and G. W. Dorn II (2009)
Circulation 119, 1263-1271
| Abstract » | Full Text » | PDF »
69
Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes.
A. Li, J. Walling, S. Ahn, Y. Kotliarov, Q. Su, M. Quezado, J. C. Oberholtzer, J. Park, J. C. Zenklusen, and H. A. Fine (2009)
Cancer Res. 69, 2091-2099
| Abstract » | Full Text » | PDF »
70
Breast Cancer Cell Lines Contain Functional Cancer Stem Cells with Metastatic Capacity and a Distinct Molecular Signature.
E. Charafe-Jauffret, C. Ginestier, F. Iovino, J. Wicinski, N. Cervera, P. Finetti, M.-H. Hur, M. E. Diebel, F. Monville, J. Dutcher, et al. (2009)
Cancer Res. 69, 1302-1313
| Abstract » | Full Text » | PDF »
71
Bladder Cancer-Associated Gene Expression Signatures Identified by Profiling of Exfoliated Urothelia.
C. J. Rosser, L. Liu, Y. Sun, P. Villicana, M. McCullers, S. Porvasnik, P. R. Young, A. S. Parker, and S. Goodison (2009)
Cancer Epidemiol. Biomarkers Prev. 18, 444-453
| Abstract » | Full Text » | PDF »
72
A genetic programming-based approach to the classification of multiclass microarray datasets.
K.-H. Liu and C.-G. Xu (2009)
Bioinformatics 25, 331-337
| Abstract » | Full Text » | PDF »
73
Thyroid Hormone Receptor {beta}1 Acts as a Potent Suppressor of Tumor Invasiveness and Metastasis.
O. Martinez-Iglesias, S. Garcia-Silva, S. P. Tenbaum, J. Regadera, F. Larcher, J. M. Paramio, B. Vennstrom, and A. Aranda (2009)
Cancer Res. 69, 501-509
| Abstract » | Full Text » | PDF »
74
Molecular Classification and Prognostication of Adrenocortical Tumors by Transcriptome Profiling.
T. J. Giordano, R. Kuick, T. Else, P. G. Gauger, M. Vinco, J. Bauersfeld, D. Sanders, D. G. Thomas, G. Doherty, and G. Hammer (2009)
Clin. Cancer Res. 15, 668-676
| Abstract » | Full Text » | PDF »
75
A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects.
B. J. Wouters, B. Lowenberg, and R. Delwel (2009)
Blood 113, 291-298
| Abstract » | Full Text » | PDF »
76
Needle and surgical biopsy techniques differentially affect adipose tissue gene expression profiles.
D. M Mutch, J. Tordjman, V. Pelloux, B. Hanczar, C. Henegar, C. Poitou, N. Veyrie, J.-D. Zucker, and K. Clement (2009)
Am. J. Clinical Nutrition 89, 51-57
| Abstract » | Full Text » | PDF »
77
The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis.
I. Takeuchi, H. Tagawa, A. Tsujikawa, M. Nakagawa, M. Katayama-Suguro, Y. Guo, and M. Seto (2009)
Haematologica 94, 61-69
| Abstract » | Full Text » | PDF »
78
Stratification of pediatric ALL by in vitro cellular responses to DNA double-strand breaks provides insight into the molecular mechanisms underlying clinical response.
E. Marston, V. Weston, J. Jesson, E. Maina, C. McConville, A. Agathanggelou, A. Skowronska, K. Mapp, K. Sameith, J. E. Powell, et al. (2009)
Blood 113, 117-126
| Abstract » | Full Text » | PDF »
79
Translational Bioinformatics: Coming of Age.
A. J Butte (2008)
JAMIA 15, 709-714
| Abstract » | Full Text » | PDF »
80
Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.
E. J. Cosgrove, Y. Zhou, T. S. Gardner, and E. D. Kolaczyk (2008)
Bioinformatics 24, 2482-2490
| Abstract » | Full Text » | PDF »
81
Antitumor activity and molecular effects of the novel heat shock protein 90 inhibitor, IPI-504, in pancreatic cancer.
D. Song, R. Chaerkady, A. C. Tan, E. Garcia-Garcia, A. Nalli, A. Suarez-Gauthier, F. Lopez-Rios, X. F. Zhang, A. Solomon, J. Tong, et al. (2008)
Mol. Cancer Ther. 7, 3275-3284
| Abstract » | Full Text » | PDF »
82
Intraplatform Reproducibility and Technical Precision of Gene Expression Profiling in 4 Laboratories Investigating 160 Leukemia Samples: The DACH Study.
A. Kohlmann, E. Haschke-Becher, B. Wimmer, A. Huber-Wechselberger, S. Meyer-Monard, H. Huxol, U. Siegler, M. Rossier, T. Matthes, M. Rebsamen, et al. (2008)
Clin. Chem. 54, 1705-1715
| Abstract » | Full Text » | PDF »
84
Gene Expression Signatures Predictive of Early Response and Outcome in High-Risk Childhood Acute Lymphoblastic Leukemia: A Children's Oncology Group Study.
D. Bhojwani, H. Kang, R. X. Menezes, W. Yang, H. Sather, N. P. Moskowitz, D.-J. Min, J. W. Potter, R. Harvey, S. P. Hunger, et al. (2008)
J. Clin. Oncol. 26, 4376-4384
| Abstract » | Full Text » | PDF »
86
Manipulation of immune system via immortal bone marrow stem cells.
C. Ruedl, H. J. Khameneh, and K. Karjalainen (2008)
Int. Immunol. 20, 1211-1218
| Abstract » | Full Text » | PDF »
http://www.sciencemag.org/cgi/content/full/286/5439/531
Medicininiai 86
1
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Gene Expression Analysis Reveals a Gene Set Discriminatory to Different Metals in Soil.
B. Nota, R. A. Verweij, D. Molenaar, B. Ylstra, N. M. van Straalen, and D. Roelofs (2010)
Toxicol. Sci. 115, 34-40
| Abstract » | Full Text » | PDF »
3
Refocusing the War on Cancer: The Critical Role of Personalized Treatment.
A. Potti, R. L. Schilsky, and J. R. Nevins (2010)
Science Translational Medicine 2, 28cm13
| Full Text » | PDF »
4
A pathway-based classification of human breast cancer.
M. L. Gatza, J. E. Lucas, W. T. Barry, J. W. Kim, Q. Wang, M. D. Crawford, M. B. Datto, M. Kelley, B. Mathey-Prevot, A. Potti, et al. (2010)
PNAS 107, 6994-6999
| Abstract » | Full Text » | PDF »
7
Gene Expression-Based Classification As an Independent Predictor of Clinical Outcome in Juvenile Myelomonocytic Leukemia.
S. Bresolin, M. Zecca, C. Flotho, L. Trentin, A. Zangrando, L. Sainati, J. Stary, B. de Moerloose, H. Hasle, C. M. Niemeyer, et al. (2010)
J. Clin. Oncol. 28, 1919-1927
| Abstract » | Full Text » | PDF »
8
Leukemogenic transformation by HOXA cluster genes.
C. Bach, S. Buhl, D. Mueller, M. P. Garcia-Cuellar, E. Maethner, and R. K. Slany (2010)
Blood 115, 2910-2918
| Abstract » | Full Text » | PDF »
10
Differential Protein Expression Profiles in Estrogen Receptor-Positive and -Negative Breast Cancer Tissues Using Label-Free Quantitative Proteomics.
K. Rezaul, J. K. Thumar, D. H. Lundgren, J. K. Eng, K. P. Claffey, L. Wilson, and D. K. Han (2010)
Genes & Cancer 1, 251-271
| Abstract » | Full Text » | PDF »
11
A motif-based analysis of glycan array data to determine the specificities of glycan-binding proteins.
A. Porter, T. Yue, L. Heeringa, S. Day, E. Suh, and B. B Haab (2010)
Glycobiology 20, 369-380
| Abstract » | Full Text » | PDF »
16
Age- and Sex-Specific Genomic Profiles in Non-Small Cell Lung Cancer.
W. Mostertz, M. Stevenson, C. Acharya, I. Chan, K. Walters, W. Lamlertthon, W. Barry, J. Crawford, J. Nevins, and A. Potti (2010)
JAMA 303, 535-543
| Abstract » | Full Text » | PDF »
18
Quantitative Proteomic Profiling of Prostate Cancer Reveals a Role for miR-128 in Prostate Cancer.
A. P. Khan, L. M. Poisson, V. B. Bhat, D. Fermin, R. Zhao, S. Kalyana-Sundaram, G. Michailidis, A. I. Nesvizhskii, G. S. Omenn, A. M. Chinnaiyan, et al. (2010)
Mol. Cell. Proteomics 9, 298-312
| Abstract » | Full Text » | PDF »
22
Reversing HOXA9 Oncogene Activation by PI3K Inhibition: Epigenetic Mechanism and Prognostic Significance in Human Glioblastoma.
B. M. Costa, J. S. Smith, Y. Chen, J. Chen, H. S. Phillips, K. D. Aldape, G. Zardo, J. Nigro, C. D. James, J. Fridlyand, et al. (2010)
Cancer Res. 70, 453-462
| Abstract » | Full Text » | PDF »
27
Characterizing the Clinical Relevance of an Embryonic Stem Cell Phenotype in Lung Adenocarcinoma.
M. Stevenson, W. Mostertz, C. Acharya, W. Kim, K. Walters, W. Barry, K. Higgins, S. A. Tuchman, J. Crawford, G. Vlahovic, et al. (2009)
Clin. Cancer Res. 15, 7553-7561
| Abstract » | Full Text » | PDF »
29
Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival.
D. Bogunovic, D. W. O'Neill, I. Belitskaya-Levy, V. Vacic, Y.-L. Yu, S. Adams, F. Darvishian, R. Berman, R. Shapiro, A. C. Pavlick, et al. (2009)
PNAS 106, 20429-20434
| Abstract » | Full Text » | PDF »
30
Global Transcriptional Response to Spermine, a Component of the Intramacrophage Environment, Reveals Regulation of Francisella Gene Expression through Insertion Sequence Elements.
P. E. Carlson Jr., J. Horzempa, D. M. O'Dee, C. M. Robinson, P. Neophytou, A. Labrinidis, and G. J. Nau (2009)
J. Bacteriol. 191, 6855-6864
| Abstract » | Full Text » | PDF »
32
Modeling the functional heterogeneity of leukemia stem cells: role of STAT5 in leukemia stem cell self-renewal.
M. Heuser, L. M. Sly, B. Argiropoulos, F. Kuchenbauer, C. Lai, A. Weng, M. Leung, G. Lin, C. Brookes, S. Fung, et al. (2009)
Blood 114, 3983-3993
| Abstract » | Full Text » | PDF »
33
ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing.
G. Quon and Q. Morris (2009)
Bioinformatics 25, 2882-2889
| Abstract » | Full Text » | PDF »
34
Genoproteomic Mining of Urothelial Cancer Suggests {gamma}-Glutamyl Hydrolase and Diazepam-Binding Inhibitor as Putative Urinary Markers of Outcome after Chemotherapy.
C. Pollard, M. Nitz, A. Baras, P. Williams, C. Moskaluk, and D. Theodorescu (2009)
Am. J. Pathol. 175, 1824-1830
| Abstract » | Full Text » | PDF »
35
The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data.
V. G. Oehler, K. Y. Yeung, Y. E. Choi, R. E. Bumgarner, A. E. Raftery, and J. P. Radich (2009)
Blood 114, 3292-3298
| Abstract » | Full Text » | PDF »
36
Variable selection and dependency networks for genomewide data.
A. Dobra (2009)
Biostat. 10, 621-639
| Abstract » | Full Text » | PDF »
37
HOXA9 Modulates Its Oncogenic Partner Meis1 To Influence Normal Hematopoiesis.
Y.-L. Hu, S. Fong, C. Ferrell, C. Largman, and W.-F. Shen (2009)
Mol. Cell. Biol. 29, 5181-5192
| Abstract » | Full Text » | PDF »
38
New Molecular Classifications of Breast Cancer.
M. Cianfrocca and W. Gradishar (2009)
CA Cancer J Clin 59, 303-313
| Abstract » | Full Text » | PDF »
39
Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation.
J. Tung, O. Fedrigo, R. Haygood, S. Mukherjee, and G. A. Wray (2009)
Mol. Biol. Evol. 26, 2047-2059
| Abstract » | Full Text » | PDF »
40
Organization of the autoantibody repertoire in healthy newborns and adults revealed by system level informatics of antigen microarray data.
A. Madi, I. Hecht, S. Bransburg-Zabary, Y. Merbl, A. Pick, M. Zucker-Toledano, F. J. Quintana, A. I. Tauber, I. R. Cohen, and E. Ben-Jacob (2009)
PNAS 106, 14484-14489
| Abstract » | Full Text » | PDF »
41
High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications.
S. Ocak, M. L. Sos, R. K. Thomas, and P. P. Massion (2009)
Eur. Respir. J. 34, 489-506
| Abstract » | Full Text » | PDF »
42
Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging.
G. Alexe, J. Monaco, S. Doyle, A. Basavanhally, A. Reddy, M. Seiler, S. Ganesan, G. Bhanot, and A. Madabhushi (2009)
Exp Biol Med 234, 860-879
| Abstract » | Full Text » | PDF »
44
Molecular Imaging of the Breast.
R. L. Birdwell, C. E. Mountford, and J. D. Iglehart (2009)
Am. J. Roentgenol. 193, 367-376
| Abstract » | Full Text » | PDF »
45
Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome.
K. I. Mills, A. Kohlmann, P. M. Williams, L. Wieczorek, W.-m. Liu, R. Li, W. Wei, D. T. Bowen, H. Loeffler, J. M. Hernandez, et al. (2009)
Blood 114, 1063-1072
| Abstract » | Full Text » | PDF »
46
A Network Model of a Cooperative Genetic Landscape in Brain Tumors.
M. Bredel, D. M. Scholtens, G. R. Harsh, C. Bredel, J. P. Chandler, J. J. Renfrow, A. K. Yadav, H. Vogel, A. C. Scheck, R. Tibshirani, et al. (2009)
JAMA 302, 261-275
| Abstract » | Full Text » | PDF »
47
Transcriptional profiling enables molecular classification of adrenocortical tumours.
C. Laurell, D. Velazquez-Fernandez, K. Lindsten, C. Juhlin, U. Enberg, J. Geli, A. Hoog, M. Kjellman, J. Lundeberg, B. Hamberger, et al. (2009)
Eur. J. Endocrinol. 161, 141-152
| Abstract » | Full Text » | PDF »
48
Comparative Analysis of the Membrane Proteome of Closely Related Metastatic and Nonmetastatic Tumor Cells.
C. Roesli, B. Borgia, C. Schliemann, M. Gunthert, H. Wunderli-Allenspach, R. Giavazzi, and D. Neri (2009)
Cancer Res. 69, 5406-5414
| Abstract » | Full Text » | PDF »
49
Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction.
C. Cheng, K. Shen, C. Song, J. Luo, and G. C. Tseng (2009)
Bioinformatics 25, 1655-1661
| Abstract » | Full Text » | PDF »
50
Identification of AML1-ETO modulators by chemical genomics.
S. M. Corsello, G. Roti, K. N. Ross, K. T. Chow, I. Galinsky, D. J. DeAngelo, R. M. Stone, A. L. Kung, T. R. Golub, and K. Stegmaier (2009)
Blood 113, 6193-6205
| Abstract » | Full Text » | PDF »
51
Efficacy and pharmacodynamic effects of bosutinib (SKI-606), a Src/Abl inhibitor, in freshly generated human pancreas cancer xenografts.
W. A. Messersmith, N.V. Rajeshkumar, A. C. Tan, X. F. Wang, V. Diesl, S. E. Choe, M. Follettie, C. Coughlin, F. Boschelli, E. Garcia-Garcia, et al. (2009)
Mol. Cancer Ther. 8, 1484-1493
| Abstract » | Full Text » | PDF »
52
Perspectives of gene expression profiling for diagnosis and therapy in haematological malignancies.
U. Bacher, A. Kohlmann, and T. Haferlach (2009)
Briefings in Functional Genomics
| Abstract » | Full Text » | PDF »
53
Cyclodepsipeptide toxin promotes the degradation of Hsp90 client proteins through chaperone-mediated autophagy.
S. Shen, P. Zhang, M. A. Lovchik, Y. Li, L. Tang, Z. Chen, R. Zeng, D. Ma, J. Yuan, and Q. Yu (2009)
J. Cell Biol. 185, 629-639
| Abstract » | Full Text » | PDF »
54
Uncovering multiple molecular targets for caffeine using a drug target validation strategy combining A2A receptor knockout mice with microarray profiling.
L. Yu, J. E. Coelho, X. Zhang, Y. Fu, A. Tillman, U. Karaoz, B. B. Fredholm, Z. Weng, and J.-F. Chen (2009)
Physiol Genomics 37, 199-210
| Abstract » | Full Text » | PDF »
55
Four-Gene Expression Ratio Test for Survival in Patients Undergoing Surgery for Mesothelioma.
G. J. Gordon, L. Dong, B. Y. Yeap, W. G. Richards, J. N. Glickman, H. Edenfield, M. Mani, R. Colquitt, G. Maulik, B. Van Oss, et al. (2009)
J Natl Cancer Inst 101, 678-686
| Abstract » | Full Text » | PDF »
56
A roadmap of clustering algorithms: finding a match for a biomedical application.
B. Andreopoulos, A. An, X. Wang, and M. Schroeder (2009)
Brief Bioinform 10, 297-314
| Abstract » | Full Text » | PDF »
57
Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips.
Y.-S. Lee, C.-H. Chen, C.-N. Tsai, C.-L. Tsai, A. Chao, and T.-H. Wang (2009)
Nucleic Acids Res. 37, e61
| Abstract » | Full Text » | PDF »
58
MIST: Maximum Information Spanning Trees for dimension reduction of biological data sets.
B. M. King and B. Tidor (2009)
Bioinformatics 25, 1165-1172
| Abstract » | Full Text » | PDF »
59
Global Levels of Histone Modifications Predict Prognosis in Different Cancers.
D. B. Seligson, S. Horvath, M. A. McBrian, V. Mah, H. Yu, S. Tze, Q. Wang, D. Chia, L. Goodglick, and S. K. Kurdistani (2009)
Am. J. Pathol. 174, 1619-1628
| Abstract » | Full Text » | PDF »
60
Regulation of mir-196b by MLL and its overexpression by MLL fusions contributes to immortalization.
R. Popovic, L. E. Riesbeck, C. S. Velu, A. Chaubey, J. Zhang, N. J. Achille, F. E. Erfurth, K. Eaton, J. Lu, H. L. Grimes, et al. (2009)
Blood 113, 3314-3322
| Abstract » | Full Text » | PDF »
61
Tissue Biomarkers for Prognosis in Cutaneous Melanoma: A Systematic Review and Meta-analysis.
B. E. G. Rothberg, M. B. Bracken, and D. L. Rimm (2009)
J Natl Cancer Inst 101, 452-474
| Abstract » | Full Text » | PDF »
62
Identification of differential gene pathways with principal component analysis.
S. Ma and M. R. Kosorok (2009)
Bioinformatics 25, 882-889
| Abstract » | Full Text » | PDF »
63
Utilizing the Molecular Gateway: The Path to Personalized Cancer Management.
J. B. Overdevest, D. Theodorescu, and J. K. Lee (2009)
Clin. Chem. 55, 684-697
| Abstract » | Full Text » | PDF »
64
Characterizing the developmental pathways TTF-1, NKX2-8, and PAX9 in lung cancer.
D. S. Hsu, C. R. Acharya, B. S. Balakumaran, R. F. Riedel, M. K. Kim, M. Stevenson, S. Tuchman, S. Mukherjee, W. Barry, H. K. Dressman, et al. (2009)
PNAS 106, 5312-5317
| Abstract » | Full Text » | PDF »
65
Molecular profiling of classical Hodgkin lymphoma tissues uncovers variations in the tumor microenvironment and correlations with EBV infection and outcome.
B. Chetaille, F. Bertucci, P. Finetti, B. Esterni, A. Stamatoullas, J. M. Picquenot, M. C. Copin, F. Morschhauser, O. Casasnovas, T. Petrella, et al. (2009)
Blood 113, 2765-3775
| Abstract » | Full Text » | PDF »
66
Drug Discovery in Psychiatric Illness: Mining for Gold.
G. I. Elmer and N. Kafkafi (2009)
Schizophr Bull
| Abstract » | Full Text » | PDF »
67
HOXA9 is required for survival in human MLL-rearranged acute leukemias.
J. Faber, A. V. Krivtsov, M. C. Stubbs, R. Wright, T. N. Davis, M. van den Heuvel-Eibrink, C. M. Zwaan, A. L. Kung, and S. A. Armstrong (2009)
Blood 113, 2375-2385
| Abstract » | Full Text » | PDF »
68
Reciprocal Regulation of Myocardial microRNAs and Messenger RNA in Human Cardiomyopathy and Reversal of the microRNA Signature by Biomechanical Support.
S. J. Matkovich, D. J. Van Booven, K. A. Youker, G. Torre-Amione, A. Diwan, W. H. Eschenbacher, L. E. Dorn, M. A. Watson, K. B. Margulies, and G. W. Dorn II (2009)
Circulation 119, 1263-1271
| Abstract » | Full Text » | PDF »
69
Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes.
A. Li, J. Walling, S. Ahn, Y. Kotliarov, Q. Su, M. Quezado, J. C. Oberholtzer, J. Park, J. C. Zenklusen, and H. A. Fine (2009)
Cancer Res. 69, 2091-2099
| Abstract » | Full Text » | PDF »
70
Breast Cancer Cell Lines Contain Functional Cancer Stem Cells with Metastatic Capacity and a Distinct Molecular Signature.
E. Charafe-Jauffret, C. Ginestier, F. Iovino, J. Wicinski, N. Cervera, P. Finetti, M.-H. Hur, M. E. Diebel, F. Monville, J. Dutcher, et al. (2009)
Cancer Res. 69, 1302-1313
| Abstract » | Full Text » | PDF »
71
Bladder Cancer-Associated Gene Expression Signatures Identified by Profiling of Exfoliated Urothelia.
C. J. Rosser, L. Liu, Y. Sun, P. Villicana, M. McCullers, S. Porvasnik, P. R. Young, A. S. Parker, and S. Goodison (2009)
Cancer Epidemiol. Biomarkers Prev. 18, 444-453
| Abstract » | Full Text » | PDF »
72
A genetic programming-based approach to the classification of multiclass microarray datasets.
K.-H. Liu and C.-G. Xu (2009)
Bioinformatics 25, 331-337
| Abstract » | Full Text » | PDF »
73
Thyroid Hormone Receptor {beta}1 Acts as a Potent Suppressor of Tumor Invasiveness and Metastasis.
O. Martinez-Iglesias, S. Garcia-Silva, S. P. Tenbaum, J. Regadera, F. Larcher, J. M. Paramio, B. Vennstrom, and A. Aranda (2009)
Cancer Res. 69, 501-509
| Abstract » | Full Text » | PDF »
74
Molecular Classification and Prognostication of Adrenocortical Tumors by Transcriptome Profiling.
T. J. Giordano, R. Kuick, T. Else, P. G. Gauger, M. Vinco, J. Bauersfeld, D. Sanders, D. G. Thomas, G. Doherty, and G. Hammer (2009)
Clin. Cancer Res. 15, 668-676
| Abstract » | Full Text » | PDF »
75
A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects.
B. J. Wouters, B. Lowenberg, and R. Delwel (2009)
Blood 113, 291-298
| Abstract » | Full Text » | PDF »
76
Needle and surgical biopsy techniques differentially affect adipose tissue gene expression profiles.
D. M Mutch, J. Tordjman, V. Pelloux, B. Hanczar, C. Henegar, C. Poitou, N. Veyrie, J.-D. Zucker, and K. Clement (2009)
Am. J. Clinical Nutrition 89, 51-57
| Abstract » | Full Text » | PDF »
77
The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis.
I. Takeuchi, H. Tagawa, A. Tsujikawa, M. Nakagawa, M. Katayama-Suguro, Y. Guo, and M. Seto (2009)
Haematologica 94, 61-69
| Abstract » | Full Text » | PDF »
78
Stratification of pediatric ALL by in vitro cellular responses to DNA double-strand breaks provides insight into the molecular mechanisms underlying clinical response.
E. Marston, V. Weston, J. Jesson, E. Maina, C. McConville, A. Agathanggelou, A. Skowronska, K. Mapp, K. Sameith, J. E. Powell, et al. (2009)
Blood 113, 117-126
| Abstract » | Full Text » | PDF »
79
Translational Bioinformatics: Coming of Age.
A. J Butte (2008)
JAMIA 15, 709-714
| Abstract » | Full Text » | PDF »
80
Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.
E. J. Cosgrove, Y. Zhou, T. S. Gardner, and E. D. Kolaczyk (2008)
Bioinformatics 24, 2482-2490
| Abstract » | Full Text » | PDF »
81
Antitumor activity and molecular effects of the novel heat shock protein 90 inhibitor, IPI-504, in pancreatic cancer.
D. Song, R. Chaerkady, A. C. Tan, E. Garcia-Garcia, A. Nalli, A. Suarez-Gauthier, F. Lopez-Rios, X. F. Zhang, A. Solomon, J. Tong, et al. (2008)
Mol. Cancer Ther. 7, 3275-3284
| Abstract » | Full Text » | PDF »
82
Intraplatform Reproducibility and Technical Precision of Gene Expression Profiling in 4 Laboratories Investigating 160 Leukemia Samples: The DACH Study.
A. Kohlmann, E. Haschke-Becher, B. Wimmer, A. Huber-Wechselberger, S. Meyer-Monard, H. Huxol, U. Siegler, M. Rossier, T. Matthes, M. Rebsamen, et al. (2008)
Clin. Chem. 54, 1705-1715
| Abstract » | Full Text » | PDF »
84
Gene Expression Signatures Predictive of Early Response and Outcome in High-Risk Childhood Acute Lymphoblastic Leukemia: A Children's Oncology Group Study.
D. Bhojwani, H. Kang, R. X. Menezes, W. Yang, H. Sather, N. P. Moskowitz, D.-J. Min, J. W. Potter, R. Harvey, S. P. Hunger, et al. (2008)
J. Clin. Oncol. 26, 4376-4384
| Abstract » | Full Text » | PDF »
86
Manipulation of immune system via immortal bone marrow stem cells.
C. Ruedl, H. J. Khameneh, and K. Karjalainen (2008)
Int. Immunol. 20, 1211-1218
| Abstract » | Full Text » | PDF »
Matematiniai straipsniai
Tinklų analizė
Getting connected: analysis and principles of biological networks
1. Xiaowei Zhu1,2,
2. Mark Gerstein3, and
3. Michael Snyder1,2,4
http://genesdev.cshlp.org/content/21/9/1010.full
http://www.sciencemag.org/cgi/content/full/286/5439/531
Matematiniai straipsniai skirti piktybinių susirgimų klasifikavimui molekuliniame lygyje pagal 1999 m Golub etc straipsnį
2
Reducing the algorithmic variability in transcriptome-based inference.
S. Tuna and M. Niranjan (2010)
Bioinformatics 26, 1185-1191
| Abstract » | Full Text » | PDF »
5
Exploring the within- and between-class correlation distributions for tumor classification.
X. Wei and K. C. Li (2010)
PNAS 107, 6737-6742
| Abstract » | Full Text » | PDF »
6
Bayesian approach to transforming public gene expression repositories into disease diagnosis databases.
H. Huang, C. C. Liu, and X. J. Zhou (2010)
PNAS 107, 6823-6828
| Abstract » | Full Text » | PDF »
9
Robust depth-based tools for the analysis of gene expression data.
S. Lopez-Pintado, J. Romo, and A. Torrente (2010)
Biostat. 11, 254-264
| Abstract » | Full Text » | PDF »
12
The maximal data piling direction for discrimination.
J. Ahn and J. S. Marron (2010)
Biometrika 97, 254-259
| Abstract » | PDF »
13
Bayesian rule learning for biomedical data mining.
V. Gopalakrishnan, J. L. Lustgarten, S. Visweswaran, and G. F. Cooper (2010)
Bioinformatics 26, 668-675
| Abstract » | Full Text » | PDF »
14
Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.
B. Xie, W. Pan, and X. Shen (2010)
Bioinformatics 26, 501-508
| Abstract » | Full Text » | PDF »
15
Functional embedding for the classification of gene expression profiles.
P.-S. Wu and H.-G. Muller (2010)
Bioinformatics 26, 509-517
| Abstract » | Full Text » | PDF »
17
Survival analysis with high-dimensional covariates.
D. M Witten and R. Tibshirani (2010)
Statistical Methods in Medical Research 19, 29-51
| Abstract » | PDF »
19
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.
T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys (2010)
Bioinformatics 26, 392-398
| Abstract » | Full Text » | PDF »
20
Dynamically weighted clustering with noise set.
Y. Shen, W. Sun, and K.-C. Li (2010)
Bioinformatics 26, 341-347
| Abstract » | Full Text » | PDF »
21
Accessible Reproducible Research.
J. P. Mesirov (2010)
Science 327, 415-416
| Abstract » | Full Text » | PDF »
23
Bayesian variable selection for disease classification using gene expression data.
Y. Ai-Jun and S. Xin-Yuan (2010)
Bioinformatics 26, 215-222
| Abstract » | Full Text » | PDF »
24
MIRAGAA--a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer.
R. K. Gaire, J. Bailey, J. Bearfoot, I. G. Campbell, P. J. Stuckey, and I. Haviv (2010)
Bioinformatics 26, 161-167
| Abstract » | Full Text » | PDF »
25
Advances in translational bioinformatics: computational approaches for the hunting of disease genes.
M. G. Kann (2010)
Brief Bioinform 11, 96-110
| Abstract » | Full Text » | PDF »
26
Advances in metaheuristics for gene selection and classification of microarray data.
B. Duval and J.-K. Hao (2010)
Brief Bioinform 11, 127-141
| Abstract » | Full Text » | PDF »
28
Multiple testing and its applications to microarrays.
Yongchao Ge, S. C Sealfon, and T. P Speed (2009)
Statistical Methods in Medical Research 18, 543-563
| Abstract » | PDF »
31
Feature selection by higher criticism thresholding achieves the optimal phase diagram.
D. Donoho and J. Jin (2009)
Phil Trans R Soc A 367, 4449-4470
| Abstract » | Full Text » | PDF »
36
Variable selection and dependency networks for genomewide data.
A. Dobra (2009)
Biostat. 10, 621-639
| Abstract » | Full Text » | PDF »
43
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data.
G. Li, Q. Ma, H. Tang, A. H. Paterson, and Y. Xu (2009)
Nucleic Acids Res. 37, e101
| Abstract » | Full Text » | PDF »
56
A roadmap of clustering algorithms: finding a match for a biomedical application.
B. Andreopoulos, A. An, X. Wang, and M. Schroeder (2009)
Brief Bioinform 10, 297-314
| Abstract » | Full Text » | PDF »
62
Identification of differential gene pathways with principal component analysis.
S. Ma and M. R. Kosorok (2009)
Bioinformatics 25, 882-889
| Abstract » | Full Text » | PDF »
72
A genetic programming-based approach to the classification of multiclass microarray datasets.
K.-H. Liu and C.-G. Xu (2009)
Bioinformatics 25, 331-337
| Abstract » | Full Text » | PDF »
83
Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.
D. Donoho and J. Jin (2008)
PNAS 105, 14790-14795
| Abstract » | Full Text » | PDF »
85
Penalized feature selection and classification in bioinformatics.
S. Ma and J. Huang (2008)
Brief Bioinform 9, 392-403
| Abstract » | Full Text » | PDF »
Getting connected: analysis and principles of biological networks
1. Xiaowei Zhu1,2,
2. Mark Gerstein3, and
3. Michael Snyder1,2,4
http://genesdev.cshlp.org/content/21/9/1010.full
http://www.sciencemag.org/cgi/content/full/286/5439/531
Matematiniai straipsniai skirti piktybinių susirgimų klasifikavimui molekuliniame lygyje pagal 1999 m Golub etc straipsnį
2
Reducing the algorithmic variability in transcriptome-based inference.
S. Tuna and M. Niranjan (2010)
Bioinformatics 26, 1185-1191
| Abstract » | Full Text » | PDF »
5
Exploring the within- and between-class correlation distributions for tumor classification.
X. Wei and K. C. Li (2010)
PNAS 107, 6737-6742
| Abstract » | Full Text » | PDF »
6
Bayesian approach to transforming public gene expression repositories into disease diagnosis databases.
H. Huang, C. C. Liu, and X. J. Zhou (2010)
PNAS 107, 6823-6828
| Abstract » | Full Text » | PDF »
9
Robust depth-based tools for the analysis of gene expression data.
S. Lopez-Pintado, J. Romo, and A. Torrente (2010)
Biostat. 11, 254-264
| Abstract » | Full Text » | PDF »
12
The maximal data piling direction for discrimination.
J. Ahn and J. S. Marron (2010)
Biometrika 97, 254-259
| Abstract » | PDF »
13
Bayesian rule learning for biomedical data mining.
V. Gopalakrishnan, J. L. Lustgarten, S. Visweswaran, and G. F. Cooper (2010)
Bioinformatics 26, 668-675
| Abstract » | Full Text » | PDF »
14
Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.
B. Xie, W. Pan, and X. Shen (2010)
Bioinformatics 26, 501-508
| Abstract » | Full Text » | PDF »
15
Functional embedding for the classification of gene expression profiles.
P.-S. Wu and H.-G. Muller (2010)
Bioinformatics 26, 509-517
| Abstract » | Full Text » | PDF »
17
Survival analysis with high-dimensional covariates.
D. M Witten and R. Tibshirani (2010)
Statistical Methods in Medical Research 19, 29-51
| Abstract » | PDF »
19
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.
T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys (2010)
Bioinformatics 26, 392-398
| Abstract » | Full Text » | PDF »
20
Dynamically weighted clustering with noise set.
Y. Shen, W. Sun, and K.-C. Li (2010)
Bioinformatics 26, 341-347
| Abstract » | Full Text » | PDF »
21
Accessible Reproducible Research.
J. P. Mesirov (2010)
Science 327, 415-416
| Abstract » | Full Text » | PDF »
23
Bayesian variable selection for disease classification using gene expression data.
Y. Ai-Jun and S. Xin-Yuan (2010)
Bioinformatics 26, 215-222
| Abstract » | Full Text » | PDF »
24
MIRAGAA--a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer.
R. K. Gaire, J. Bailey, J. Bearfoot, I. G. Campbell, P. J. Stuckey, and I. Haviv (2010)
Bioinformatics 26, 161-167
| Abstract » | Full Text » | PDF »
25
Advances in translational bioinformatics: computational approaches for the hunting of disease genes.
M. G. Kann (2010)
Brief Bioinform 11, 96-110
| Abstract » | Full Text » | PDF »
26
Advances in metaheuristics for gene selection and classification of microarray data.
B. Duval and J.-K. Hao (2010)
Brief Bioinform 11, 127-141
| Abstract » | Full Text » | PDF »
28
Multiple testing and its applications to microarrays.
Yongchao Ge, S. C Sealfon, and T. P Speed (2009)
Statistical Methods in Medical Research 18, 543-563
| Abstract » | PDF »
31
Feature selection by higher criticism thresholding achieves the optimal phase diagram.
D. Donoho and J. Jin (2009)
Phil Trans R Soc A 367, 4449-4470
| Abstract » | Full Text » | PDF »
36
Variable selection and dependency networks for genomewide data.
A. Dobra (2009)
Biostat. 10, 621-639
| Abstract » | Full Text » | PDF »
43
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data.
G. Li, Q. Ma, H. Tang, A. H. Paterson, and Y. Xu (2009)
Nucleic Acids Res. 37, e101
| Abstract » | Full Text » | PDF »
56
A roadmap of clustering algorithms: finding a match for a biomedical application.
B. Andreopoulos, A. An, X. Wang, and M. Schroeder (2009)
Brief Bioinform 10, 297-314
| Abstract » | Full Text » | PDF »
62
Identification of differential gene pathways with principal component analysis.
S. Ma and M. R. Kosorok (2009)
Bioinformatics 25, 882-889
| Abstract » | Full Text » | PDF »
72
A genetic programming-based approach to the classification of multiclass microarray datasets.
K.-H. Liu and C.-G. Xu (2009)
Bioinformatics 25, 331-337
| Abstract » | Full Text » | PDF »
83
Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.
D. Donoho and J. Jin (2008)
PNAS 105, 14790-14795
| Abstract » | Full Text » | PDF »
85
Penalized feature selection and classification in bioinformatics.
S. Ma and J. Huang (2008)
Brief Bioinform 9, 392-403
| Abstract » | Full Text » | PDF »
2010 m. balandžio 26 d., pirmadienis
2010 balandžio 26 d.
Prisiminiau ir patyrinėjau piktybinių susirgimų klasifikaciją: pagal WHO C00 - D 48, pagal histologiją - sarkomos...karcinomos, pagal kliniką - TNM ir nauja - daugybė bandymų klasifikuoti pagal genų aktyvumą. Turbūt, tai, apie ką buvau užsiminęs savo preprinte 1989 m. Pionieriškas straipsnis šia tema pasirodė tik 1999 m.-
href="http://www.sciencemag.org/cgi/content/full/286/5439/531
Science 15 October 1999:
Vol. 286. no. 5439, pp. 531 - 537
DOI: 10.1126/science.286.5439.531
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
T. R. Golub, 1,2* D. K. Slonim, 1 P. Tamayo, 1 C. Huard, 1 M. Gaasenbeek, 1 J. P. Mesirov, 1 H. Coller, 1 M. L. Loh, 2 J. R. Downing, 3 M. A. Caligiuri, 4 C. D. Bloomfield, 4 E. S. Lander 1,5*
Jo esmė - ...the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
1. recently, particular subtypes of acute leukemia have been found to be associated with specific chromosomal translocations--for example, the t(12;21)(p13;q22) translocation occurs in 25% of patients with ALL, whereas the t(8;21)(q22;q22) occurs in 15% of patients with AML
2. Although the distinction between AML and ALL has been well established, no single test is currently sufficient to establish the diagnosis. Rather, current clinical practice involves an experienced hematopathologist's interpretation of the tumor's morphology, histochemistry, immunophenotyping, and cytogenetic analysis, each performed in a separate, highly specialized laboratory. Although usually accurate, leukemia classification remains imperfect and errors do occur.
Distinguishing ALL from AML is critical for successful treatment; chemotherapy regimens for ALL generally contain corticosteroids, vincristine, methotrexate, and L-asparaginase, whereas most AML regimens rely on a backbone of daunorubicin and cytarabine (8). Although remissions can be achieved using ALL therapy for AML (and vice versa), cure rates are markedly diminished, and unwarranted toxicities are encountered.
We set out to develop a more systematic approach to cancer classification based on the simultaneous expression monitoring of thousands of genes using DNA microarrays (9). It has been suggested (10) that such microarrays could provide a tool for cancer classification.
Our initial leukemia data set consisted of 38 bone marrow samples (27 ALL, 11 AML) obtained from acute leukemia patients at the time of diagnosis
RNA prepared from bone marrow mononuclear cells was hybridized to high-density oligonucleotide microarrays, produced by Affymetrix and containing probes for 6817 human genes (14). The 6817 genes were sorted by their degree of correlation (16). To establish whether the observed correlations were stronger than would be expected by chance, we developed a method called "neighborhood analysis" (Fig. 1A).
We applied this approach to the 38 acute leukemia samples. The set of informative genes to be used in the predictor was chosen to be the 50 genes most closely correlated with AML-ALL distinction in the known samples.
The list of informative genes used in the AML versus ALL predictor was highly instructive (Fig. 3B). Some, including CD11c, CD33, and MB-1, encode cell surface proteins for which monoclonal antibodies have been demonstrated to be useful in distinguishing lymphoid from myeloid lineage cells (25). Others provide new markers of acute leukemia subtype. For example, the leptin receptor, originally identified through its role in weight regulation, showed high relative expression in AML. The leptin receptor was recently demonstrated to have anti-apoptotic function in hematopoietic cells (26). Similarly, the zyxin gene has been shown to encode a LIM domain protein important in cell adhesion in fibroblasts, but a role in hematopoiesis has not been reported (27).
We had expected that the genes most useful in AML-ALL class prediction would simply be markers of hematopoietic lineage, and would not necessarily be related to cancer pathogenesis. However, many of the genes encode proteins critical for S-phase cell cycle progression (Cyclin D3, Op18, and MCM3), chromatin remodeling (RbAp48 and SNF2), transcription (TFIIE), and cell adhesion (zyxin and CD11c) or are known oncogenes (c-MYB, E2A and HOXA9). In addition, one of the informative genes encodes topoisomerase II, which is the principal target of the antileukemic drug etoposide (28). Together, these data suggest that genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology.
The list of informative genes used in the AML versus ALL predictor was highly instructive (Fig. 3B). Some, including CD11c, CD33, and MB-1, encode cell surface proteins for which monoclonal antibodies have been demonstrated to be useful in distinguishing lymphoid from myeloid lineage cells (25). Others provide new markers of acute leukemia subtype. For example, the leptin receptor, originally identified through its role in weight regulation, showed high relative expression in AML. The leptin receptor was recently demonstrated to have anti-apoptotic function in hematopoietic cells (26). Similarly, the zyxin gene has been shown to encode a LIM domain protein important in cell adhesion in fibroblasts, but a role in hematopoiesis has not been reported (27).
We had expected that the genes most useful in AML-ALL class prediction would simply be markers of hematopoietic lineage, and would not necessarily be related to cancer pathogenesis. However, many of the genes encode proteins critical for S-phase cell cycle progression (Cyclin D3, Op18, and MCM3), chromatin remodeling (RbAp48 and SNF2), transcription (TFIIE), and cell adhesion (zyxin and CD11c) or are known oncogenes (c-MYB, E2A and HOXA9). In addition, one of the informative genes encodes topoisomerase II, which is the principal target of the antileukemic drug etoposide (28). Together, these data suggest that genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology.
PROBLEMA - daugiamatiškumas ir atsitiktinių skirtumų vaidmuo bei perteklinių įtakos, eliminavimas. Šiaip, straipsnis labai įdomus ir dėl rezultatų, kurių absoliutus patikimumas nėra aiškus ir dėl metodologijos - bandymo klasifikuoti ALL ir AML pagal genų ekspresiją.
Pilną tekstą pavyko aptikti Science bibliotekoje,
http://www.sciencemag.org
kurioje užsiregistravau, kaip urba.
Kiek daug naujos literatūros, faktų - ir, klaidžiojimų tamsose.
Yra ir books - free anglų.k,
http://www.edom.co.uk/info/free_ebooks.html?cx=partner-pub-1473261655401871%3A9ob06gyh3xl&cof=FORID%3A10&ie=ISO-8859-1&q=+Molecular+biology+cancer+2010&sa=+++Search+++&siteurl=www.edom.co.uk%252Finfo%252F%253Fgclid%253DCOrfiOW2pKECFUgh3wodCWCIyQ#1168
atrodo, studentams, nors, kai ko vertingo irgi galima rasti. "Užknisa" - purchase - bet pasaulis jau toks yra - už daug ką reikia mokėti, o dalinasi, dažniausiai, tik daug turintys.
Prisiminiau ir patyrinėjau piktybinių susirgimų klasifikaciją: pagal WHO C00 - D 48, pagal histologiją - sarkomos...karcinomos, pagal kliniką - TNM ir nauja - daugybė bandymų klasifikuoti pagal genų aktyvumą. Turbūt, tai, apie ką buvau užsiminęs savo preprinte 1989 m. Pionieriškas straipsnis šia tema pasirodė tik 1999 m.-
href="http://www.sciencemag.org/cgi/content/full/286/5439/531
Science 15 October 1999:
Vol. 286. no. 5439, pp. 531 - 537
DOI: 10.1126/science.286.5439.531
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
T. R. Golub, 1,2* D. K. Slonim, 1 P. Tamayo, 1 C. Huard, 1 M. Gaasenbeek, 1 J. P. Mesirov, 1 H. Coller, 1 M. L. Loh, 2 J. R. Downing, 3 M. A. Caligiuri, 4 C. D. Bloomfield, 4 E. S. Lander 1,5*
Jo esmė - ...the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
1. recently, particular subtypes of acute leukemia have been found to be associated with specific chromosomal translocations--for example, the t(12;21)(p13;q22) translocation occurs in 25% of patients with ALL, whereas the t(8;21)(q22;q22) occurs in 15% of patients with AML
2. Although the distinction between AML and ALL has been well established, no single test is currently sufficient to establish the diagnosis. Rather, current clinical practice involves an experienced hematopathologist's interpretation of the tumor's morphology, histochemistry, immunophenotyping, and cytogenetic analysis, each performed in a separate, highly specialized laboratory. Although usually accurate, leukemia classification remains imperfect and errors do occur.
Distinguishing ALL from AML is critical for successful treatment; chemotherapy regimens for ALL generally contain corticosteroids, vincristine, methotrexate, and L-asparaginase, whereas most AML regimens rely on a backbone of daunorubicin and cytarabine (8). Although remissions can be achieved using ALL therapy for AML (and vice versa), cure rates are markedly diminished, and unwarranted toxicities are encountered.
We set out to develop a more systematic approach to cancer classification based on the simultaneous expression monitoring of thousands of genes using DNA microarrays (9). It has been suggested (10) that such microarrays could provide a tool for cancer classification.
Our initial leukemia data set consisted of 38 bone marrow samples (27 ALL, 11 AML) obtained from acute leukemia patients at the time of diagnosis
RNA prepared from bone marrow mononuclear cells was hybridized to high-density oligonucleotide microarrays, produced by Affymetrix and containing probes for 6817 human genes (14). The 6817 genes were sorted by their degree of correlation (16). To establish whether the observed correlations were stronger than would be expected by chance, we developed a method called "neighborhood analysis" (Fig. 1A).
We applied this approach to the 38 acute leukemia samples. The set of informative genes to be used in the predictor was chosen to be the 50 genes most closely correlated with AML-ALL distinction in the known samples.
The list of informative genes used in the AML versus ALL predictor was highly instructive (Fig. 3B). Some, including CD11c, CD33, and MB-1, encode cell surface proteins for which monoclonal antibodies have been demonstrated to be useful in distinguishing lymphoid from myeloid lineage cells (25). Others provide new markers of acute leukemia subtype. For example, the leptin receptor, originally identified through its role in weight regulation, showed high relative expression in AML. The leptin receptor was recently demonstrated to have anti-apoptotic function in hematopoietic cells (26). Similarly, the zyxin gene has been shown to encode a LIM domain protein important in cell adhesion in fibroblasts, but a role in hematopoiesis has not been reported (27).
We had expected that the genes most useful in AML-ALL class prediction would simply be markers of hematopoietic lineage, and would not necessarily be related to cancer pathogenesis. However, many of the genes encode proteins critical for S-phase cell cycle progression (Cyclin D3, Op18, and MCM3), chromatin remodeling (RbAp48 and SNF2), transcription (TFIIE), and cell adhesion (zyxin and CD11c) or are known oncogenes (c-MYB, E2A and HOXA9). In addition, one of the informative genes encodes topoisomerase II, which is the principal target of the antileukemic drug etoposide (28). Together, these data suggest that genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology.
The list of informative genes used in the AML versus ALL predictor was highly instructive (Fig. 3B). Some, including CD11c, CD33, and MB-1, encode cell surface proteins for which monoclonal antibodies have been demonstrated to be useful in distinguishing lymphoid from myeloid lineage cells (25). Others provide new markers of acute leukemia subtype. For example, the leptin receptor, originally identified through its role in weight regulation, showed high relative expression in AML. The leptin receptor was recently demonstrated to have anti-apoptotic function in hematopoietic cells (26). Similarly, the zyxin gene has been shown to encode a LIM domain protein important in cell adhesion in fibroblasts, but a role in hematopoiesis has not been reported (27).
We had expected that the genes most useful in AML-ALL class prediction would simply be markers of hematopoietic lineage, and would not necessarily be related to cancer pathogenesis. However, many of the genes encode proteins critical for S-phase cell cycle progression (Cyclin D3, Op18, and MCM3), chromatin remodeling (RbAp48 and SNF2), transcription (TFIIE), and cell adhesion (zyxin and CD11c) or are known oncogenes (c-MYB, E2A and HOXA9). In addition, one of the informative genes encodes topoisomerase II, which is the principal target of the antileukemic drug etoposide (28). Together, these data suggest that genes useful for cancer class prediction may also provide insight into cancer pathogenesis and pharmacology.
PROBLEMA - daugiamatiškumas ir atsitiktinių skirtumų vaidmuo bei perteklinių įtakos, eliminavimas. Šiaip, straipsnis labai įdomus ir dėl rezultatų, kurių absoliutus patikimumas nėra aiškus ir dėl metodologijos - bandymo klasifikuoti ALL ir AML pagal genų ekspresiją.
Pilną tekstą pavyko aptikti Science bibliotekoje,
http://www.sciencemag.org
kurioje užsiregistravau, kaip urba.
Kiek daug naujos literatūros, faktų - ir, klaidžiojimų tamsose.
Yra ir books - free anglų.k,
http://www.edom.co.uk/info/free_ebooks.html?cx=partner-pub-1473261655401871%3A9ob06gyh3xl&cof=FORID%3A10&ie=ISO-8859-1&q=+Molecular+biology+cancer+2010&sa=+++Search+++&siteurl=www.edom.co.uk%252Finfo%252F%253Fgclid%253DCOrfiOW2pKECFUgh3wodCWCIyQ#1168
atrodo, studentams, nors, kai ko vertingo irgi galima rasti. "Užknisa" - purchase - bet pasaulis jau toks yra - už daug ką reikia mokėti, o dalinasi, dažniausiai, tik daug turintys.
Užsisakykite:
Komentarai (Atom)