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arXiv 2019年

作者： Kang, John Thompson, Reid Aneja, Sanjay Lehman, Constance Trister, Andrew Zou, James Obcemea, Ceferino El Naqa, Issam Dept. of Radiation Oncology University of Rochester Medical Center NY United States Dept. of Radiation Medicine Oregon Health & Science University PortlandOR United States Portland Healthcare System VA United States Dept of Therapeutic Radiology Yale University New HavenNY United States Dept. of Radiology Harvard Medical School Mass General Hospital BostonMA United States Bill & Melinda Gates Foundation SeattleWA United States Dept. of Biomedical Data Science Stanford University. Chan-Zuckerberg Biohub San FranciscoCA Radiation Research Program National Cancer Institute BethesdaMD Singapore Dept. of Radiation Oncology University of Michigan Ann ArborMI United States

Artificial intelligence (AI) is about to touch every aspect of radiotherapy from consultation, treatment planning, quality assurance, therapy delivery, to outcomes reporting. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd artificial intelligence solutions into clinical practice. Poorly-trained personnel may do more harm than good when attempting to apply rapidly-developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. In this perspective article by the Training and Education Working Group from the National cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019), we provide and discuss Action Points relevant for future trainees interested in radiation oncology artificial intelligence: (1) creating AI awareness and proper conduct;(2) developing a dedicated didactic curriculum;(3) creating a publicly available database of training resources;and (4) accelerating the translation process of AI into clinical practice. Copyright © 2019, The Authors. All rights reserved.

关键词： Radiotherapy

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Author Correction: A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing

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Nature communications 2022年第1期13卷 2828页

作者： Sanju Sinha Karina Barbosa Kuoyuan Cheng Mark D M Leiserson Prashant Jain Anagha Deshpande David M Wilson Bríd M Ryan Ji Luo Ze'ev A Ronai Joo Sang Lee Aniruddha J Deshpande Eytan Ruppin Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. Laboratory of Human Carcinogenesis Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20850 USA. Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 USA. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. Laboratory of Molecular Gerontology National Institute on Aging Intramural Research Program National Institutes of Health Baltimore MD 21224 USA. Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institute of Health Bethesda MD 20850 USA. Samsung Medical Center Sungkyunkwan University School of Medicine Suwon 16419 Republic of Korea. Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 Republic of Korea. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. adeshpande@***. Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. eytan.ruppin@nih.gov.

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk

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European journal of human genetics : EJHG 2022年第5期30卷 630-631页

作者： Eileen O Dareng Jonathan P Tyrer Daniel R Barnes Michelle R Jones Xin Yang Katja K H Aben Muriel A Adank Simona Agata Irene L Andrulis Hoda Anton-Culver Natalia N Antonenkova Gerasimos Aravantinos Banu K Arun Annelie Augustinsson Judith Balmaña Elisa V Bandera Rosa B Barkardottir Daniel Barrowdale Matthias W Beckmann Alicia Beeghly-Fadiel Javier Benitez Marina Bermisheva Marcus Q Bernardini Line Bjorge Amanda Black Natalia V Bogdanova Bernardo Bonanni Ake Borg James D Brenton Agnieszka Budzilowska Ralf Butzow Saundra S Buys Hui Cai Maria A Caligo Ian Campbell Rikki Cannioto Hayley Cassingham Jenny Chang-Claude Stephen J Chanock Kexin Chen Yoke-Eng Chiew Wendy K Chung Kathleen B M Claes Sarah Colonna Linda S Cook Fergus J Couch Mary B Daly Fanny Dao Eleanor Davies Miguel de la Hoya Robin de Putter Joe Dennis Allison DePersia Peter Devilee Orland Diez Yuan Chun Ding Jennifer A Doherty Susan M Domchek Thilo Dörk Andreas du Bois Matthias Dürst Diana M Eccles Heather A Eliassen Christoph Engel Gareth D Evans Peter A Fasching James M Flanagan Renée T Fortner Eva Machackova Eitan Friedman Patricia A Ganz Judy Garber Francesca Gensini Graham G Giles Gord Glendon Andrew K Godwin Marc T Goodman Mark H Greene Jacek Gronwald Eric Hahnen Christopher A Haiman Niclas Håkansson Ute Hamann Thomas V O Hansen Holly R Harris Mikael Hartman Florian Heitz Michelle A T Hildebrandt Estrid Høgdall Claus K Høgdall John L Hopper Ruea-Yea Huang Chad Huff Peter J Hulick David G Huntsman Evgeny N Imyanitov Claudine Isaacs Anna Jakubowska Paul A James Ramunas Janavicius Allan Jensen Oskar Th Johannsson Esther M John Michael E Jones Daehee Kang Beth Y Karlan Anthony Karnezis Linda E Kelemen Elza Khusnutdinova Lambertus A Kiemeney Byoung-Gie Kim Susanne K Kjaer Ian Komenaka Jolanta Kupryjanczyk Allison W Kurian Ava Kwong Diether Lambrechts Melissa C Larson Conxi Lazaro Nhu D Le Goska Leslie Jenny Lester Fabienne Lesueur Douglas A Levine Lian Li Jingmei Li Jennifer T Loud Karen H Lu Jan Lubiński Phuong L Mai Siranoush Manoukian Jeffrey R Marks Ra University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK. University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK. Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA. Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands. Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands. The Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands. Veneto Institute of Oncology IOV-IRCCS Immunology and Molecular Oncology Unit Padua Italy. Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada. University of Toronto Department of Molecular Genetics Toronto ON Canada. University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA. N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus. 'Agii Anargiri' Cancer Hospital Athens Greece. University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA. Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden. Vall d'Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain. University Hospital of Vall d'Hebron Department of Medical Oncology Barcelona Spain. Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA. Landspitali University Hospital Department of Pathology Reykjavik Iceland. University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland. University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany. Vanderbilt University School of Medicine Division of Epide

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Publisher Correction: A T cell resilience model associated with response to immunotherapy in multiple tumor types

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Nature medicine 2022年第10期28卷 2219页

作者： Yu Zhang Trang Vu Douglas C Palmer Rigel J Kishton Lanqi Gong Jiao Huang Thanh Nguyen Zuojia Chen Cari Smith Ferenc Livák Rohit Paul Chi-Ping Day Chuan Wu Glenn Merlino Kenneth Aldape Xin-Yuan Guan Peng Jiang Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. Department of Clinical Oncology The University of Hong Kong Hong Kong China. Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. Surgery Branch National Cancer Institute National Institutes of Health Bethesda MD USA. AstraZeneca Gaithersburg MD USA. Lyell Immunopharma South San Francisco CA USA. Laboratory of Pathology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. Gaia Foods Singapore Singapore. Experimental Immunology Branch Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. Laboratory Animal Science Program Leidos Biomedical Research Inc Frederick MD USA. Flow Cytometry Core Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. Office of the Director Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD USA. peng.jiang@nih.gov.

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Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

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Nature genetics 2023年第3期55卷 519-520页

作者： Ceres Fernandez-Rozadilla Maria Timofeeva Zhishan Chen Philip Law Minta Thomas Stephanie Schmit Virginia Díez-Obrero Li Hsu Juan Fernandez-Tajes Claire Palles Kitty Sherwood Sarah Briggs Victoria Svinti Kevin Donnelly Susan Farrington James Blackmur Peter Vaughan-Shaw Xiao-Ou Shu Jirong Long Qiuyin Cai Xingyi Guo Yingchang Lu Peter Broderick James Studd Jeroen Huyghe Tabitha Harrison David Conti Christopher Dampier Mathew Devall Fredrick Schumacher Marilena Melas Gad Rennert Mireia Obón-Santacana Vicente Martín-Sánchez Ferran Moratalla-Navarro Jae Hwan Oh Jeongseon Kim Sun Ha Jee Keum Ji Jung Sun-Seog Kweon Min-Ho Shin Aesun Shin Yoon-Ok Ahn Dong-Hyun Kim Isao Oze Wanqing Wen Keitaro Matsuo Koichi Matsuda Chizu Tanikawa Zefang Ren Yu-Tang Gao Wei-Hua Jia John Hopper Mark Jenkins Aung Ko Win Rish Pai Jane Figueiredo Robert Haile Steven Gallinger Michael Woods Polly Newcomb David Duggan Jeremy Cheadle Richard Kaplan Timothy Maughan Rachel Kerr David Kerr Iva Kirac Jan Böhm Lukka-Pekka Mecklin Pekka Jousilahti Paul Knekt Lauri Aaltonen Harri Rissanen Eero Pukkala Johan Eriksson Tatiana Cajuso Ulrika Hänninen Johanna Kondelin Kimmo Palin Tomas Tanskanen Laura Renkonen-Sinisalo Brent Zanke Satu Männistö Demetrius Albanes Stephanie Weinstein Edward Ruiz-Narvaez Julie Palmer Daniel Buchanan Elizabeth Platz Kala Visvanathan Cornelia Ulrich Erin Siegel Stefanie Brezina Andrea Gsur Peter Campbell Jenny Chang-Claude Michael Hoffmeister Hermann Brenner Martha Slattery John Potter Konstantinos Tsilidis Matthias Schulze Marc Gunter Neil Murphy Antoni Castells Sergi Castellví-Bel Leticia Moreira Volker Arndt Anna Shcherbina Mariana Stern Bens Pardamean Timothy Bishop Graham Giles Melissa Southey Gregory Idos Kevin McDonnell Zomoroda Abu-Ful Joel Greenson Katerina Shulman Flavio Lejbkowicz Kenneth Offit Yu-Ru Su Robert Steinfelder Temitope Keku Bethany van Guelpen Thomas Hudson Heather Hampel Rachel Pearlman Sonja Berndt Richard Hayes Marie Elena Martinez Sushma Thomas Douglas Corley Paul Pharoah Susanna Larsson Yun Yen Heinz-Jo Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK. Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain. Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA. Division of Genetics and Epidemiology Institute of Cancer Research London UK. Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA. Genomic Medicine Institute Cleveland Clinic Cleveland OH USA. Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA. Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain. Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain. Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain. Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain. Department of Biostatistics School of Public Health University of Washington Seattle WA USA. Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK. Department of Public Health Richard Doll Building University of Oxford Oxford UK. Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA. Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA. Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA. De

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Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

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Nucleic Acids Research 2024年第3期52卷 E17页

作者： Chang, Lan-Yun Lee, Meng-Zhan Wu, Yujia Lee, Wen-Kai Ma, Chia-Liang Chang, Jun-Mao Chen, Ciao-Wen Huang, Tzu-Chun Lee, Chia-Hwa Lee, Jih-Chin Tseng, Yu-Yao Lin, Chun-Yu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 300 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan Department of Food Science Nutrition and Nutraceutical Biotechnology Shih Chien University Taipei 104 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Cancer and Immunology Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge. © 2024 Oxford University Press. All rights reserved.

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DDX5 and its associated lncRNA Rmrp modulate T_H17 cell effector functions

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Nature 2015年第7583期528卷 517-522页

作者： Huang, Wendy Thomas, Benjamin Flynn, Ryan A. Gavzy, Samuel J. Wu, Lin Kim, Sangwon V. Hall, Jason A. Miraldi, Emily R. Ng, Charles P. Rigo, Frank W. Meadows, Sarah Montoya, Nina R. Herrera, Natalia G. Domingos, Ana I. Rastinejad, Fraydoon Myers, Richard M. Fuller-Pace, Frances V. Bonneau, Richard Chang, Howard Y. Acuto, Oreste Littman, Dan R. Kimmel Center for Biology and Medicine Skirball Institute New York University School of Medicine New York 10016 NY United States Sir William Dunn School of Pathology University of Oxford Oxford OX1 3RE United Kingdom Center for Personal Dynamic Regulomes Stanford University Stanford 94305 CA United States Center for Genomics and Systems Biology Department of Biology New York University New York 10003 NY United States Courant Institute of Mathematical Sciences Computer Science Department New York University New York 10012 NY United States Simons Center for Data Analysis Simons Foundation New York 10010 NY United States Isis Pharmaceuticals Carlsbad 92010 CA United States HudsonAlpha Institute for Biotechnology Huntsville 35806 AL United States Instituto Gulbenkian de Ciencia Oeiras 2780-156 Portugal Integrative Metabolism Program Sanford Burnham Prebys Medical Discovery Institute Orlando 32827 FL United States Division of Cancer Research University of Dundee Dundee DD1 9SY United Kingdom Howard Hughes Medical Institute New York University School of Medicine New York 10016 NY United States

T helper 17 (TH17) lymphocytes protect mucosal barriers from infections, but also contribute to multiple chronic inflammatory diseases. Their differentiation is controlled by RORγt, a ligand-regulated nuclear recepto...

T helper 17 (T_H17) lymphocytes protect mucosal barriers from infections, but also contribute to multiple chronic inflammatory diseases. Their differentiation is controlled by RORγt, a ligand-regulated nuclear receptor. Here we identify the RNA helicase DEAD-box protein 5 (DDX5) as a RORγt partner that coordinates transcription of selective T_H17 genes, and is required for T_H17-mediated inflammatory pathologies. Surprisingly, the ability of DDX5 to interact with RORγt and coactivate its targets depends on intrinsic RNA helicase activity and binding of a conserved nuclear long noncoding RNA (lncRNA), Rmrp, which is mutated in patients with cartilage-hair hypoplasia. A targeted Rmrp gene mutation in mice, corresponding to a gene mutation in cartilage-hair hypoplasia patients, altered lncRNA chromatin occupancy, and reduced the DDX5-RORγt interaction and RORγt target gene transcription. Elucidation of the link between Rmrp and the DDX5-RORγt complex reveals a role for RNA helicases and lncRNAs in tissue-specific transcriptional regulation, and provides new opportunities for therapeutic intervention in T_H17-dependent diseases. © 2015 Macmillan Publishers Limited. All rights reserved.

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Categorization and analysis of 14 computational methods for estimating cell potency from single-cell RNA-seq data

arXiv

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arXiv 2023年

作者： Wang, Qingyang Zhai, Zhiqian Lian, Qiuyu Song, Dongyuan Li, Jingyi Jessica Department of Statistics and Data Science University of California Los AngelesCA90095-1554 United States Wellcome Trust Cancer Research UK Gurdon Institute University of Cambridge Cambridgeshire CambridgeCB2 1QN United Kingdom Bioinformatics Interdepartmental Ph.D. Program University of California Los AngelesCA90095-7246 United States Department of Human Genetics University of California Los AngelesCA90095-7088 United States Department of Computational Medicine University of California Los AngelesCA90095-1766 United States Department of Biostatistics University of California Los AngelesCA90095-1772 United States Radcliffe Institute for Advanced Study Harvard University CambridgeMA02138 United States

In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden cellular dynamic processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring pseudotime trajectories and RNA velocities, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as the "cell potency." In this review, we provide a comprehensive summary of 14 computational methods that define cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: average-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition for each method. Moreover, we use a unified set of mathematical notations to detail the 14 methods’ methodologies and summarize their usage complexities, including the number of parameters, the number of required inputs, and the changes from the method description in publications to the method implementation in software packages. In conclusion, we regard cell potency estimation as an open question that lacks a consensus on the optimal approach, calling for benchmark datasets and studies. Through categorization and detailed description of existing methods, we aim to provide a solid foundation for future benchmark studies of cell potency estimation methods. A broader open challenge is to understand the comparative advantages of methods that infer cellular dynamics from scRNA-seq data using diverse perspectives, including pseudotime trajectories, RNA velocities, and cell potency. © 2023, CC BY-NC-ND.

关键词： Gene expression

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. MacDermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, David L. Chodera, John D. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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