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A compendium of next-generation patient-derived models for diverse cancers
The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models1,2. …
Dina ElHarouni, Mushriq Al-Jazrawe, Seongmin Choi, Merve Dede, Toshinori Hinoue, Sean a Misek, Heeju Noh, Luca Zanella, Yuen-Yi Tseng, Hayley E Francies, Dennis Plenker, Cindy W Kyi, Julyann Perez-Mayoral, Megan J Stine, Eva Tonsing-Carter, Rachana Agarwal, Jean Claude Zenklusen, James M Clinton, Jennifer M Shelton, Timothy R Chu, William F Hooper, Xavi Loinaz, Paula Keskula, Jordan Tagle, Peyton C Kuhlers, Bahar Tercan, Sylvia F Boj, Alessandro Vasciaveo, Lorenzo Tomassoni, James M Crawford, Shawna Walsh, Claire Sinai, Sonam Bhatia, Priya Sridevi, Hardik Patel, Maria Antonietta Cerone, HCMI Network, Kyle Ellrott, Calvin J Kuo, Olivier Elemento, Semir Beyaz, Vincenzo Corbo, David L Spector, Rameen Beroukhim, Martin L Ferguson, Andrew D Cherniack, Peter W Laird, Nicolas Robine, Andrew McPherson, Katherine a Hoadley, Mathew J Garnett, David a Tuveson, Andrea Califano, Paul T Spellman, Keith L Ligon, Daniela S Gerhard, Louis M Staudt, Jesse S Boehm
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Eleven quick tips for Biomedical Federated Learning
Modern statistical and machine learning techniques are effective at describing, testing hypotheses and making predictions from complex …
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Towards a decentralized future for open-science databases
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Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal
AI-based embeddings offer the possibilities of encoding complex biological data into low-dimensional spaces, called embedding spaces, …
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A decentralized future for the open-science databases
The continuous and reliable open access to curated biological data repositories is indispensable for accelerating rigorous scientific …
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Robust software development practices improve citations of RNA-seq tools
RNA sequencing (RNA-seq) has emerged as an exemplary technology in biology and clinical applications, offering a crucial complement to …
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Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles
Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity …
Bhavneet Bhinder, Verena Friedl, Sunantha Sethuraman, Davide Risso, Kami E Chiotti, R Jay Mashl, Kyle P Ellrott, Jordan a Lee, Christopher K Wong, Kofi Gyan, Aditya Deshpande, Marcin Imielinski, Rohan Bareja, Josh Stuart, Myron Peto, Katherine a Hoadley, Alexander J Lazar, Andrew D Cherniack, Jingchun Zhu, Shaolong Cao, Mark Rubin, Wenyi Wang, Oliver F Bathe, Nicolas Robine, Li Ding, Peter W Laird, Wanding Zhou, Hui Shen, Vésteinn Thorsson, Jen Jen Yeh, Matthew H Bailey, Daniel Cui Zhou, Xianlu L Peng, Mary Goldman, Yongsheng Li, Anil Korkut, Nidhi Sahni, D Neil Hayes, Michael K a Mensah, Ina Felau, Anab Kemal, Samantha Caesar-Johnson, John a Demchok, Liming Yang, Martin L Ferguson, Roy Tarnuzzer, Zhining Wang, Jean C Zenklusen, Cancer Genome Atlas Analysis Network, Paul Spellman, Olivier Elemento
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Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods
As genes tend to be co-regulated as gene modules, feature selection in machine learning (ML) on gene expression data can be challenged …
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Protocol for obtaining cancer type and subtype predictions using subSCOPE
We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps …
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Aggregating multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal
AI based embeddings offer the possibilities of encoding complex biological data into low dimensional spaces, called embedding spaces, …
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