A citation-based method for searching scientific literature

Jianzhu Ma, Samson H Fong, Yunan Luo, Christopher J Bakkenist, John Paul Shen, Soufiane Mourragui, Lodewyk F A Wessels, Marc Hafner, Roded Sharan, Jian Peng, Trey Ideker. Nat Cancer 2021
Times Cited: 16







List of co-cited articles
87 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


A community effort to assess and improve drug sensitivity prediction algorithms.
James C Costello, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad-ud-din, Petteri Hintsanen, Suleiman A Khan,[...]. Nat Biotechnol 2014
365
31

MOLI: multi-omics late integration with deep neural networks for drug response prediction.
Hossein Sharifi-Noghabi, Olga Zolotareva, Colin C Collins, Martin Ester. Bioinformatics 2019
58
31


Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.
Brent M Kuenzi, Jisoo Park, Samson H Fong, Kyle S Sanchez, John Lee, Jason F Kreisberg, Jianzhu Ma, Trey Ideker. Cancer Cell 2020
37
31

The Cancer Genome Atlas Pan-Cancer analysis project.
John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Mills Shaw, Brad A Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, Joshua M Stuart. Nat Genet 2013
31

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.
Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A Margolin, Sungjoon Kim, Christopher J Wilson, Joseph Lehár, Gregory V Kryukov, Dmitriy Sonkin,[...]. Nature 2012
25


A Landscape of Pharmacogenomic Interactions in Cancer.
Francesco Iorio, Theo A Knijnenburg, Daniel J Vis, Graham R Bignell, Michael P Menden, Michael Schubert, Nanne Aben, Emanuel Gonçalves, Syd Barthorpe, Howard Lightfoot,[...]. Cell 2016
772
25

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.
Aravind Subramanian, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, Scott L Pomeroy, Todd R Golub, Eric S Lander,[...]. Proc Natl Acad Sci U S A 2005
25

Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization.
Muhammad Ammad-Ud-Din, Suleiman A Khan, Disha Malani, Astrid Murumägi, Olli Kallioniemi, Tero Aittokallio, Samuel Kaski. Bioinformatics 2016
49
25

MARS: discovering novel cell types across heterogeneous single-cell experiments.
Maria Brbić, Marinka Zitnik, Sheng Wang, Angela O Pisco, Russ B Altman, Spyros Darmanis, Jure Leskovec. Nat Methods 2020
30
18

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.
Paul Geeleher, Zhenyu Zhang, Fan Wang, Robert F Gruener, Aritro Nath, Gladys Morrison, Steven Bhutra, Robert L Grossman, R Stephanie Huang. Genome Res 2017
51
18

High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.
Hui Gao, Joshua M Korn, Stéphane Ferretti, John E Monahan, Youzhen Wang, Mallika Singh, Chao Zhang, Christian Schnell, Guizhi Yang, Yun Zhang,[...]. Nat Med 2015
654
18

A Deep Learning Framework for Predicting Response to Therapy in Cancer.
Theodore Sakellaropoulos, Konstantinos Vougas, Sonali Narang, Filippos Koinis, Athanassios Kotsinas, Alexander Polyzos, Tyler J Moss, Sarina Piha-Paul, Hua Zhou, Eleni Kardala,[...]. Cell Rep 2019
47
18


Dr.VAE: improving drug response prediction via modeling of drug perturbation effects.
Ladislav Rampášek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg. Bioinformatics 2019
47
18

Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data.
In Sock Jang, Elias Chaibub Neto, Juistin Guinney, Stephen H Friend, Adam A Margolin. Pac Symp Biocomput 2014
89
18


Systematic identification of genomic markers of drug sensitivity in cancer cells.
Mathew J Garnett, Elena J Edelman, Sonja J Heidorn, Chris D Greenman, Anahita Dastur, King Wai Lau, Patricia Greninger, I Richard Thompson, Xi Luo, Jorge Soares,[...]. Nature 2012
18

Kernelized rank learning for personalized drug recommendation.
Xiao He, Lukas Folkman, Karsten Borgwardt. Bioinformatics 2018
17
18

Evaluating the molecule-based prediction of clinical drug responses in cancer.
Zijian Ding, Songpeng Zu, Jin Gu. Bioinformatics 2016
51
18

Hallmarks of cancer: the next generation.
Douglas Hanahan, Robert A Weinberg. Cell 2011
18

Using deep learning to model the hierarchical structure and function of a cell.
Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan, Trey Ideker. Nat Methods 2018
117
18

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.
Naiqian Zhang, Haiyun Wang, Yun Fang, Jun Wang, Xiaoqi Zheng, X Shirley Liu. PLoS Comput Biol 2015
76
18

Similarity network fusion for aggregating data types on a genomic scale.
Bo Wang, Aziz M Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg. Nat Methods 2014
579
18

A meta-learning approach for genomic survival analysis.
Yeping Lina Qiu, Hong Zheng, Arnout Devos, Heather Selby, Olivier Gevaert. Nat Commun 2020
9
22

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.
Patrik L Ståhl, Fredrik Salmén, Sanja Vickovic, Anna Lundmark, José Fernández Navarro, Jens Magnusson, Stefania Giacomello, Michaela Asp, Jakub O Westholm, Mikael Huss,[...]. Science 2016
722
12

Deep learning for computational biology.
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle. Mol Syst Biol 2016
473
12

Integrating spatial gene expression and breast tumour morphology via deep learning.
Bryan He, Ludvig Bergenstråhle, Linnea Stenbeck, Abubakar Abid, Alma Andersson, Åke Borg, Jonas Maaskola, Joakim Lundeberg, James Zou. Nat Biomed Eng 2020
45
12


Extended-connectivity fingerprints.
David Rogers, Mathew Hahn. J Chem Inf Model 2010
12


Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.
Marc Hafner, Mario Niepel, Mirra Chung, Peter K Sorger. Nat Methods 2016
231
12

Reproducible pharmacogenomic profiling of cancer cell line panels.
Peter M Haverty, Eva Lin, Jenille Tan, Yihong Yu, Billy Lam, Steve Lianoglou, Richard M Neve, Scott Martin, Jeff Settleman, Robert L Yauch,[...]. Nature 2016
154
12

Revisiting inconsistency in large pharmacogenomic studies.
Zhaleh Safikhani, Petr Smirnov, Mark Freeman, Nehme El-Hachem, Adrian She, Quevedo Rene, Anna Goldenberg, Nicolai J Birkbak, Christos Hatzis, Leming Shi,[...]. F1000Res 2016
35
12

Predicting in vitro drug sensitivity using Random Forests.
Gregory Riddick, Hua Song, Susie Ahn, Jennifer Walling, Diego Borges-Rivera, Wei Zhang, Howard A Fine. Bioinformatics 2011
87
12

Consistency in drug response profiling.
John Patrick Mpindi, Bhagwan Yadav, Päivi Östling, Prson Gautam, Disha Malani, Astrid Murumägi, Akira Hirasawa, Sara Kangaspeska, Krister Wennerberg, Olli Kallioniemi,[...]. Nature 2016
41
12

Drug response consistency in CCLE and CGP.
Mehdi Bouhaddou, Matthew S DiStefano, Eric A Riesel, Emilce Carrasco, Hadassa Y Holzapfel, DeAnalisa C Jones, Gregory R Smith, Alan D Stern, Sulaiman S Somani, T Victoria Thompson,[...]. Nature 2016
35
12

Machine learning approaches to drug response prediction: challenges and recent progress.
George Adam, Ladislav Rampášek, Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg. NPJ Precis Oncol 2020
37
12

Machine learning approaches to drug response prediction: challenges and recent progress.
George Adam, Ladislav Rampášek, Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg. NPJ Precis Oncol 2020
16
12

Genetic and transcriptional evolution alters cancer cell line drug response.
Uri Ben-David, Benjamin Siranosian, Gavin Ha, Helen Tang, Yaara Oren, Kunihiko Hinohara, Craig A Strathdee, Joshua Dempster, Nicholas J Lyons, Robert Burns,[...]. Nature 2018
329
12


Inconsistency in large pharmacogenomic studies.
Benjamin Haibe-Kains, Nehme El-Hachem, Nicolai Juul Birkbak, Andrew C Jin, Andrew H Beck, Hugo J W L Aerts, John Quackenbush. Nature 2013
312
12

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.
Brinton Seashore-Ludlow, Matthew G Rees, Jaime H Cheah, Murat Cokol, Edmund V Price, Matthew E Coletti, Victor Jones, Nicole E Bodycombe, Christian K Soule, Joshua Gould,[...]. Cancer Discov 2015
324
12

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics.
Hossein Sharifi-Noghabi, Shuman Peng, Olga Zolotareva, Colin C Collins, Martin Ester. Bioinformatics 2020
8
25

PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors.
Soufiane Mourragui, Marco Loog, Mark A van de Wiel, Marcel J T Reinders, Lodewyk F A Wessels. Bioinformatics 2019
12
16

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples.
Edward W Huang, Ameya Bhope, Jing Lim, Saurabh Sinha, Amin Emad. PLoS Comput Biol 2020
12
16

A cross-study analysis of drug response prediction in cancer cell lines.
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash, Thomas Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith Cohn, James Doroshow, Xiaotian Duan, Veronika Dubinkina,[...]. Brief Bioinform 2022
4
50

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies.
Petr Smirnov, Victor Kofia, Alexander Maru, Mark Freeman, Chantal Ho, Nehme El-Hachem, George-Alexandru Adam, Wail Ba-Alawi, Zhaleh Safikhani, Benjamin Haibe-Kains. Nucleic Acids Res 2018
71
12

Ensemble transfer learning for the prediction of anti-cancer drug response.
Yitan Zhu, Thomas Brettin, Yvonne A Evrard, Alexander Partin, Fangfang Xia, Maulik Shukla, Hyunseung Yoo, James H Doroshow, Rick L Stevens. Sci Rep 2020
10
20


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.