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Benjamin J. Lengerich
Researcher at Carnegie Mellon University
Publications - 32
Citations - 1802
Benjamin J. Lengerich is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 8, co-authored 25 publications receiving 1248 citations. Previous affiliations of Benjamin J. Lengerich include Pennsylvania State University & Massachusetts Institute of Technology.
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Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Journal ArticleDOI
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.
TL;DR: The Precision Lasso is a Lasso variant that promotes sparse variable selection by regularization governed by the covariance and inverse covariance matrices of explanatory variables that outperforms popular methods of variable selection such as the Lasso, the Elastic Net and Minimax Concave Penalty regression.
Posted ContentDOI
Opportunities And Obstacles For Deep Learning In Biology And Medicine
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Anthony Gitter,Casey S. Greene +26 more
TL;DR: This work examines applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges.
Journal ArticleDOI
Experimental and computational mutagenesis to investigate the positioning of a general base within an enzyme active site.
Jason P. Schwans,Philip Hanoian,Benjamin J. Lengerich,Fanny Sunden,Ana Gonzalez,Yingssu Tsai,Yingssu Tsai,Sharon Hammes-Schiffer,Daniel Herschlag +8 more
TL;DR: Recognizing the extent, type, and energetic interconnectivity of interactions that contribute to positioning catalytic groups has implications for enzyme evolution and may help reveal the nature and extent of interactions required to design enzymes that rival those found in biology.
Proceedings Article
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
TL;DR: Functional retrofitting as mentioned in this paper generalizes current retrofitting methods by explicitly modeling pairwise relations, which can directly incorporate a variety of pairwise penalty functions previously developed for knowledge graph completion and allow users to encode, learn, and extract information about relation semantics.