Format
Sort by

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 108

1.

Predicting in vitro drug sensitivity using Random Forests.

Riddick G, Song H, Ahn S, Walling J, Borges-Rivera D, Zhang W, Fine HA.

Bioinformatics. 2011 Jan 15;27(2):220-4. doi: 10.1093/bioinformatics/btq628. Epub 2010 Dec 5.

2.

The COXEN principle: translating signatures of in vitro chemosensitivity into tools for clinical outcome prediction and drug discovery in cancer.

Smith SC, Baras AS, Lee JK, Theodorescu D.

Cancer Res. 2010 Mar 1;70(5):1753-8. doi: 10.1158/0008-5472.CAN-09-3562. Epub 2010 Feb 16. Review.

3.

Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival.

Györffy B, Serra V, Jürchott K, Abdul-Ghani R, Garber M, Stein U, Petersen I, Lage H, Dietel M, Schäfer R.

Oncogene. 2005 Nov 17;24(51):7542-51.

PMID:
16044152
4.

An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

Wan Q, Pal R.

PLoS One. 2014 Jun 30;9(6):e101183. doi: 10.1371/journal.pone.0101183. eCollection 2014.

5.

Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines.

Boegsted M, Holst JM, Fogd K, Falgreen S, Sørensen S, Schmitz A, Bukh A, Johnsen HE, Nyegaard M, Dybkaer K.

PLoS One. 2011 Apr 29;6(4):e19322. doi: 10.1371/journal.pone.0019322.

6.

Prediction of anticancer drug potency from expression of genes involved in growth factor signaling.

Dai Z, Barbacioru C, Huang Y, Sadée W.

Pharm Res. 2006 Feb;23(2):336-49. Epub 2006 Jan 26.

PMID:
16425089
7.

A systematic evaluation of multi-gene predictors for the pathological response of breast cancer patients to chemotherapy.

Shen K, Song N, Kim Y, Tian C, Rice SD, Gabrin MJ, Symmans WF, Pusztai L, Lee JK.

PLoS One. 2012;7(11):e49529. doi: 10.1371/journal.pone.0049529. Epub 2012 Nov 21.

8.

A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

Haider S, Rahman R, Ghosh S, Pal R.

PLoS One. 2015 Dec 10;10(12):e0144490. doi: 10.1371/journal.pone.0144490. eCollection 2015.

9.

Mixture classification model based on clinical markers for breast cancer prognosis.

Zeng T, Liu J.

Artif Intell Med. 2010 Feb-Mar;48(2-3):129-37. doi: 10.1016/j.artmed.2009.07.008. Epub 2009 Dec 14.

PMID:
20005686
10.

Cytochrome P450 1B1 gene polymorphisms as predictors of anticancer drug activity: studies with in vitro models.

Laroche-Clary A, Le Morvan V, Yamori T, Robert J.

Mol Cancer Ther. 2010 Dec;9(12):3315-21. doi: 10.1158/1535-7163.MCT-10-0673. Epub 2010 Oct 1.

11.

Antitumor activity of histone deacetylase inhibitors in non-small cell lung cancer cells: development of a molecular predictive model.

Miyanaga A, Gemma A, Noro R, Kataoka K, Matsuda K, Nara M, Okano T, Seike M, Yoshimura A, Kawakami A, Uesaka H, Nakae H, Kudoh S.

Mol Cancer Ther. 2008 Jul;7(7):1923-30. doi: 10.1158/1535-7163.MCT-07-2140. Epub 2008 Jul 7.

12.

Genomic approach towards personalized anticancer drug therapy.

Midorikawa Y, Tsuji S, Takayama T, Aburatani H.

Pharmacogenomics. 2012 Jan;13(2):191-9. doi: 10.2217/pgs.11.157. Review.

PMID:
22256868
13.

Widespread molecular patterns associated with drug sensitivity in breast cancer cell lines, with implications for human tumors.

Creighton CJ.

PLoS One. 2013 Dec 27;8(12):e71158. doi: 10.1371/journal.pone.0071158. eCollection 2013.

14.

Gene expression patterns within cell lines are predictive of chemosensitivity.

Ring BZ, Chang S, Ring LW, Seitz RS, Ross DT.

BMC Genomics. 2008 Feb 8;9:74. doi: 10.1186/1471-2164-9-74.

15.

Computational identification of multi-omic correlates of anticancer therapeutic response.

Stetson LC, Pearl T, Chen Y, Barnholtz-Sloan JS.

BMC Genomics. 2014;15 Suppl 7:S2. doi: 10.1186/1471-2164-15-S7-S2. Epub 2014 Oct 27. Erratum in: BMC Genomics. 2015;16:481.

17.

High-throughput 3D screening reveals differences in drug sensitivities between culture models of JIMT1 breast cancer cells.

Hongisto V, Jernström S, Fey V, Mpindi JP, Kleivi Sahlberg K, Kallioniemi O, Perälä M.

PLoS One. 2013 Oct 23;8(10):e77232. doi: 10.1371/journal.pone.0077232. eCollection 2013.

18.

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery.

Lee JK, Havaleshko DM, Cho H, Weinstein JN, Kaldjian EP, Karpovich J, Grimshaw A, Theodorescu D.

Proc Natl Acad Sci U S A. 2007 Aug 7;104(32):13086-91. Epub 2007 Jul 31.

19.

Analysis of Food and Drug Administration-approved anticancer agents in the NCI60 panel of human tumor cell lines.

Holbeck SL, Collins JM, Doroshow JH.

Mol Cancer Ther. 2010 May;9(5):1451-60. doi: 10.1158/1535-7163.MCT-10-0106. Epub 2010 May 4.

20.

In vitro human cell line models to predict clinical response to anticancer drugs.

Niu N, Wang L.

Pharmacogenomics. 2015;16(3):273-85. doi: 10.2217/pgs.14.170. Review.

Items per page

Supplemental Content

Write to the Help Desk