G
Guy Nir
Researcher at Harvard University
Publications - 61
Citations - 1802
Guy Nir is an academic researcher from Harvard University. The author has contributed to research in topics: Elastography & Genome. The author has an hindex of 19, co-authored 47 publications receiving 1113 citations. Previous affiliations of Guy Nir include Bar-Ilan University & Wyss Institute for Biologically Inspired Engineering.
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Journal ArticleDOI
A new era: artificial intelligence and machine learning in prostate cancer.
TL;DR: Machine learning algorithms are able to enhance prostate cancer treatment by augmenting the surgeon’s display with information such as cancer localization during robotic procedures and other image-guided interventions and could be used towards autonomous manipulation of tools for assistance in the operating room.
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Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling
Guy Nir,Irene Farabella,Cynthia Pérez Estrada,Cynthia Pérez Estrada,Cynthia Pérez Estrada,Carl G. Ebeling,Brian J. Beliveau,Hiroshi Sasaki,Hiroshi Sasaki,S. Dean Lee,Son C. Nguyen,Ruth B. McCole,Shyamtanu Chattoraj,Jelena Erceg,Jumana AlHaj Abed,Nuno Martins,Huy Q. Nguyen,Mohammed A. Hannan,Sheikh Russell,Neva C. Durand,Neva C. Durand,Suhas S.P. Rao,Suhas S.P. Rao,Suhas S.P. Rao,Jocelyn Y. Kishi,Jocelyn Y. Kishi,Paula Soler-Vila,Michele Di Pierro,José N. Onuchic,Steven P. Callahan,John M. Schreiner,Jeff A. Stuckey,Peng Yin,Peng Yin,Erez Lieberman Aiden,Marc A. Marti-Renom,C.-ting Wu,C.-ting Wu +37 more
TL;DR: Focusing on chromosomal regions that contribute to compartments, it is discovered that distinct structures that, in spite of considerable variability, can predict whether such regions correspond to active or inactive compartments.
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OligoMiner provides a rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes
Brian J. Beliveau,Jocelyn Y. Kishi,Guy Nir,Hiroshi Sasaki,Sinem K. Saka,Son C. Nguyen,Chao-ting Wu,Peng Yin +7 more
TL;DR: A rapid and robust computational pipeline for the genome-scale design of oligo FISH probes that affords the scientist exact control over the parameters of each probe that will make the FISH probe design process much more accessible and facilitate the design of pools of hybridization probes for a variety of applications.
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Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.
Guy Nir,Soheil Hor,Davood Karimi,Ladan Fazli,Brian F. Skinnider,Peyman Tavassoli,Dmitry Turbin,Carlos F. Villamil,Gang Wang,R. Storey Wilson,Kenneth A. Iczkowski,M. Scott Lucia,Peter C. Black,Purang Abolmaesumi,S. Larry Goldenberg,Septimiu E. Salcudean +15 more
TL;DR: A computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods that incorporates a probabilistic approach for supervised learning by multiple experts to account for the inter‐observer grading variability.
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Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation
TL;DR: The study shows that well-designed and properly trained deep learning models can achieve PCa Gleason grading accuracy that is comparable to an expert pathologist.