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Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants (vol 11, 955, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 955页

作者： Donovan, Margaret K. R. D'Antonio-Chronowska, Agnieszka D'Antonio, Matteo Frazer, Kelly A. University of California San Diego Bioinformatics and Systems Biology Graduate Program La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Biomedical Informatics La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Institute for Genomic Medicine La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Gene expression Genetic association study RNA sequencing

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O63: Leveraging global and local genomic data to guide equitable carrier screening: From Genome Aggregation Database (gnomAD) to Qatar Genome

Genetics in Medicine Open

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Genetics in Medicine Open 2025年 3卷 102632-102632页

作者： Aarabi, Mahmoud Mbarek, Hamdi Bashar, Aryan Soman, Vishal Al-Mulla, Hajer Nkrumah, Esther Schmitz, Matthew Darabi, Helia Wang, John Kiehl, Paris Sethi, Rahil Saad, Chadi Rajkovic, Aleksandar Yatsenko, Svetlana Chandran, Uma Departments of Pathology and Obstetrics Gynecology and Reproductive Sciences University of Pittsburgh School of Medicine； Medical Genetics and Genomics Laboratories University of Pittsburgh Medical Center (UPMC) Pittsburgh PA Qatar Genome Program Qatar Foundation； Qatar Precision Health Institute Doha Qatar Data Science Graduate Program University of Colorado Boulder Boulder CO Department of Biomedical Informatics University of Pittsburgh School of Medicine Pittsburgh PA Joan H. Marks Graduate Program in Human Genetics Sarah Lawrence College New York NY Temerty Faculty of Medicine University of Toronto Toronto ON Canada Medical Genetics and Genomics Laboratories University of Pittsburgh Medical Center (UPMC) Department of Pediatrics University of Pittsburgh School of Medicine Pittsburgh PA Department of Microbiology Immunology and Pathology Colorado State University Fort Collins CO Department of Pathology and Institute of Human Genetics University of California San Francisco San Francisco CA Department of Pathology Stanford University School of Medicine Stanford CA

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

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BMC genomics 2024年第1期25卷 600页

作者： Ang-Chu Huang Jia-Ying Su Yu-Jen Hung Hung-Lun Chiang Yi-Ting Chen Yen-Tsung Huang Chen-Hsin Albert Yu Hsin-Nan Lin Chien-Ling Lin Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. Institute of Statistical Science Academia Sinica Taipei Taiwan. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. arith@gate.sinica.edu.tw. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. mbcllin@gate.sinica.edu.tw. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. mbcllin@gate.sinica.edu.tw. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. mbcllin@gate.sinica.edu.tw.

BACKGROUND:Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights. RESULTS:Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest. CONCLUSIONS:In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://***/SpliceAPP . Source code can be downloaded at https://***/hsinnan75/SpliceAPP .

关键词： Human mutations LASSO regression RNA splicing Splicing variant prediction

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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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Corrigendum to "Novel model of multiple sclerosis induced by EBV-like virus generate a unique B cell population" [Journal of Neuroimmunology volume 394 (2024) 578408]

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Journal of neuroimmunology 2025年 404卷 578625页

作者： J L Deffenbaugh K-J Jung S P Murphy Y Liu C N Rau C Petersen-Cherubini P L Collins D Chung A E Lovett-Racke Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA. Department of Computer Science and Engineering The Ohio State University USA. Department of Microbial Infection & Immunity The Ohio State University Wexner Medical Center USA Neuroscience Graduate Program The Ohio State University USA. Department of Biomedical Informatics The Ohio State University Wexner Medical Center USA Pelotonia Institute for Immuno-Oncology The Ohio State University Wexner Medical Center USA. Department of Neuroscience The Ohio State University Wexner Medical Center USA. Electronic address: amylovettracke@***.

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Personal health records - Potential one-stop approach in polypharmacy

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Computer Methods and programs in Biomedicine Update 2022年 2卷

作者： Chen, Ya-Lin Satria, Fauzi Budi Jamshed, Shazia Babar, Zaheer-Ud-Din Mukherjee, Nilesh Yang, Hsuan-Chia Li, Yu-Chuan Iqbal, Usman Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan Department of Pharmacy Practice Faculty of Pharmacy UniSZA Kuala Terengganu Malaysia Qualitative Research-Methodological Application in Health Sciences Research Group Kulliyyah of Pharmacy International Islamic University Malaysia Kuantan Malaysia Centre for Pharmaceutical Policy and Practice Research Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Master of Science in Systems Biology Manipal School of Life Sciences Manipal Academy of Higher Education Manipal India International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan

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A Global Genome-wide Scan with Optimal Cutoff Mining for Emerging Biomarkers in Head and Neck Squamous Cell Carcinoma

Research Square

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Research Square 2020年

作者： Chi, Li-Hsing Wu, Alexander T.H. Hsiao, Michael Li, Yu-Chuan The Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Academia Sinica Taipei Taiwan Division of Oral and Maxillofacial Surgery Department of Dentistry Taipei Medical University Hospital Taipei Taiwan Genomics Research Center Academia Sinica Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background: The survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method to discover the gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). In order to utilize a continuous gene expression for survival analysis, it is necessary to determine a cutoff point by the dichotomization of the patients. There is some optimization software for cutoff determination. However, those predetermined cutoffs by software usually set at the median, 1/4 quantile, or 3/4 quantile of RNA sequencing (RNA-Seq) value to find a significant P-value of the Kaplan-Meier curve. There are few clinicopathological features available on their pre-processed data sets. Methods: We developed a comprehensive workflow by R script, running on the Rstudio platform. It includes data retrieving and pre-processing, feature selection, cutoff mining engine, Kaplan-Meier survival analysis, Cox proportional hazard modeling, and biomarker discovery. Results: Using this workflow on the TCGA HNSCC cohort, we scanned human protein-coding genes (20,500) programmatically. After adjustment with other confounders, we found that the clinical tumor stage and the surgical margin involvement are independent risk factors in patient survival. According to the resulting tables with Bonferroni adjusted P-value under optimal cutoff as well as hazard ratio (>= 1.5), there were ten candidate biomarkers, named as DKK1, CAMK2N1, STC2, PGK1, SURF4, USP10, NDFIP1, FOXA2, STIP1, and DKC1, which are significantly associated with the poor prognosis of overall survival (OS). At the same time, the other ten genes were over-expressed in the better survival patients (with hazard ratio © 2020, CC BY.

关键词： Tumors

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Microbiota analysis in the hemodialysis population - Focusing on Enterobacteriaceae

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Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第2期56卷 311-323页

作者： Hon-Yen Wu Yi-Tsung Lin Wan-Chuan Tsai Yen-Ling Chiu Mei-Ju Ko Ju-Yeh Yang Mei-Fen Pai Hsin-Sui Hsu Shih-Ping Hsu Yu-Sen Peng Chun-Hsing Liao Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Division of Infectious Diseases Department of Medicine Taipei Veterans General Hospital Taipei City Taiwan Institute of Emergency and Critical Care Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan City Taiwan. Department of Dermatology Taipei City Hospital Taipei City Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Healthcare Administration Asia Eastern University of Science and Technology New Taipei City Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Electronic address: liaoch@femh.org.tw.

BACKGROUND:Infection is a recognized risk factor for mortality among hemodialysis (HD) population, including infection caused by Enterobacteriaceae. We aimed to investigate Enterobacteriaceae in gut microbiota among HD patients and to analyze associations between microbiota and clinical parameters. METHODS:This prospective study of microbiota analysis in HD patients was conducted in April-May 2019. A control group without recent antibiotic use or hospitalization was used for comparison. Stool samples underwent 16S rRNA sequencing, using Greengenes 16S rRNA database for microbiota analysis. RESULTS:Among 96 hemodialysis (HD) patients, mean age was 61.9 ± 0.8 years and mean duration of HD was 6.5 ± 0.7 years. No significant differences were found in alpha diversity between HD and control groups (HD group 949.5, controls 898; p = 0.16) although significant between-group differences were found in beta diversity (p < 0.001). At phylum level, HD group had a higher abundance of Firmicutes and Proteobacteria, but lower abundance of Bacteriodetes. At genus level, Escherichia-Shigella complex increased among HD patients who had hospitalization with 1 year (median 0.024 vs 0.004, p = 0.054) and Klebsiella was associated with emergency room visit within 1 year among HD patients (p = 0.002). CONCLUSIONS:Alpha diversity in HD patients is not lower than that in healthy controls but significant between-group differences are found in microbiota composition according to beta diversity, due to decreased Bacteriodetes and increased Firmicutes and Proteobacteria. Deeper microbiota analyses for Enterobacteriaceae are necessary. Whether change in dietary components can help to decrease mortality among dialysis population warrants further research.

关键词： Chronic Enterobacteriaceae Gastrointestinal microbiome Hemodialysis Kidney failure Microbiota Prognosis

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EVALUATION OF SOFTWARE IMPACT DESIGNED FOR biomedical RESEARCH: ARE WE MEASURING WHAT'S MEANINGFUL?

arXiv

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arXiv 2023年

作者： Afiaz, Awan Ivanov, Andrey A. Chamberlin, John Hanauer, David Savonen, Candace L. Goldman, Mary J. Morgan, Martin Reich, Michael Getka, Alexander Holmes, Aaron Pati, Sarthak Knight, Dan Boutros, Paul C. Bakas, Spyridon Caporaso, J. Gregory Del Fiol, Guilherme Hochheiser, Harry Haas, Brian Schloss, Patrick D. Eddy, James A. Albrecht, Jake Fedorov, Andrey Waldron, Levi Hoffman, Ava M. Bradshaw, Richard L. Leek, Jeffrey T. Wright, Carrie Department of Biostatistics University of Washington SeattleWA United States Biostatistics Program Public Health Sciences Division Fred Hutchinson Cancer Center SeattleWA United States Department of Pharmacology and Chemical Biology Emory University School of Medicine Emory University AtlantaGA United States Department of Biomedical Informatics University of Utah Salt Lake CityUT United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California Santa Cruz Santa CruzCA United States Roswell Park Comprehensive Cancer Center BuffaloNY United States University of California San Diego La Jolla CA United States University of Pennsylvania PhiladelphiaPA United States Jonsson Comprehensive Cancer Center University of California Los AngelesCA United States Institute for Precision Health University of California Los AngelesCA United States Department of Human Genetics University of California Los AngelesCA United States Department of Urology University of California Los AngelesCA United States Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Methods Development Laboratory Broad Institute CambridgeMA United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Sage Bionetworks SeattleWA United States Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Epidemiology and Biostatistics City University of New York Graduate School of Public Health and Health Policy New YorkNY United States

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that su

关键词： Diseases

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