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Research Square 2021年

作者： Zhao, Hongyu Liu, Wei Deng, Wenxuan Chen, Ming Dong, Zihan Zhu, Biqing Yu, Zhaolong Tang, Daiwei Sauler, Maor Wain, Louise V. Cho, Michael H. Kaminski, Naftali Program of Computational Biology and Bioinformatics Yale University New HavenCT06510 United States Department of Biostatistics Yale School of Public Health 60 College Street New HavenCT06510 United States Pulmonary Critical Care and Sleep Medicine Yale School of Medicine New HavenCT06510 United States Department of Health Sciences University of Leicester Leicester United Kingdom National Institute for Health Research Leicester Respiratory Biomedical Research Centre Glenfield Hospital Leicester United Kingdom Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States Pulmonary and Critical Care Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. Here, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed substantial statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs. © 2021, CC BY.

关键词： Tissue

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Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research

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Journal of Pathology Informatics 2020年第1期11卷 1-7页

作者： Durant, Thomas J.S. Gong, Guannan Price, Nathan Schulz, Wade Department of Laboratory Medicine Yale University School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation Yale New Haven Hospital New Haven CT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University School of Medicine New Haven CT United States Department of Information Technology Yale New Haven Health New Haven CT United States

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification. © 2020 Journal

关键词： Biobanking biomedical research biospecimen science clinical specimens real-time identification translational research

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Geometry based data generation 32

Geometry based data generation

引用

32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Lindenbaum, Ofir Wolf, Guy Stanley, Jay S. Krishnaswamy, Smita Applied Mathematics Program Yale University New HavenCT06511 United States Computational Biology and Bioinformatics Program Yale University New HavenCT06510 United States Departments of Genetics and Computer Science Yale University New HavenCT06510 United States

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis. © 2018 Curran Associates Inc..All rights reserved.

关键词： Genes

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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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Manifold learning-based methods for analyzing single-cell RNA-sequencing data

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Current Opinion in Systems biology 2018年 7卷 36-46页

作者： Moon, Kevin R. Stanley, Jay S. Burkhardt, Daniel van Dijk, David Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New Haven CT United States Applied Mathematics Program Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States

Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data. © 2017 Published by Elsevier Ltd.

关键词： Data mining Gene regulatory networks Imputation Manifold learning Pseudotime trends Single cell RNA sequencing

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Using physical features of protein core packing to distinguish real proteins from decoys

arXiv

引用

arXiv 2020年

作者： Grigas, Alex T. Mei, Zhe Treado, John D. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute signifficantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (speciffically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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A systems biology approach uncovers novel disease mechanisms in age-related macular degeneration

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Cell Genomics 2023年第6期3卷 100302-100302页

作者： Orozco, Luz D. Owen, Leah A. Hofmann, Jeffrey Stockwell, Amy D. Tao, Jianhua Haller, Susan Mukundan, Vineeth T. Clarke, Christine Lund, Jessica Sridhar, Akshayalakshmi Mayba, Oleg Barr, Julie L. Zavala, Rylee A. Graves, Elijah C. Zhang, Charles Husami, Nadine Finley, Robert Au, Elizabeth Lillvis, John H. Farkas, Michael H. Shakoor, Akbar Sherva, Richard Kim, Ivana K. Kaminker, Joshua S. Townsend, Michael J. Farrer, Lindsay A. Yaspan, Brian L. Chen, Hsu-Hsin DeAngelis, Margaret M. Department of Bioinformatics and Computational Biology Genentech South San Francisco 94080 CA United States Department of Ophthalmology and Visual Sciences University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Population Health Sciences University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Obstetrics and Gynecology University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Ophthalmology Ross Eye Institute Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Department of Pathology Genentech South San Francisco 94080 CA United States Department of Human Genetics Genentech South San Francisco 94080 CA United States Departments of Microchemistry Proteomics and Lipidomics Genentech South San Francisco 94080 CA United States Department of Human Pathobiology & OMNI Reverse Translation Genentech South San Francisco 94080 CA United States Neuroscience Graduate Program Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Department of Biochemistry Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Veterans Administration Western New York Healthcare System Buffalo 14212 NY United States Department of Medicine Biomedical Genetics Boston University School of Medicine Boston 02118 MA United States Retina Service Massachusetts Eye & Ear Department of Ophthalmology Harvard Medical School Boston 02114 MA United States Genetics Genomics and Bioinformatics Graduate Program Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States

Age-related macular degeneration (AMD) is a leading cause of blindness, affecting 200 million people worldwide. To identify genes that could be targeted for treatment, we created a molecular atlas at different stages of AMD. Our resource is comprised of RNA sequencing (RNA-seq) and DNA methylation microarrays from bulk macular retinal pigment epithelium (RPE)/choroid of clinically phenotyped normal and AMD donor eyes (n = 85), single-nucleus RNA-seq (164,399 cells), and single-nucleus assay for transposase-accessible chromatin (ATAC)-seq (125,822 cells) from the retina, RPE, and choroid of 6 AMD and 7 control donors. We identified 23 genome-wide significant loci differentially methylated in AMD, over 1,000 differentially expressed genes across different disease stages, and an AMD Müller state distinct from normal or gliosis. Chromatin accessibility peaks in genome-wide association study (GWAS) loci revealed putative causal genes for AMD, including HTRA1 and C6orf223. Our systems biology approach uncovered molecular mechanisms underlying AMD, including regulators of WNT signaling, FRZB and TLE2, as mechanistic players in disease. © 2023 The Authors

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Assessing individual head and neck squamous cell carcinoma patient response to therapy through integration of functional and genomic data

引用

Scientific reports 2025年第1期15卷 19742页

作者： Daniel Bottomly Chase Mathieson Myles Vigoda Sophia Jeng Nathaniel Evans Ashley Anderson Aurora Blucher Aletha Lesch Christina Zheng Ted Laderas James Jacobs Molly Kulesz-Martin Shannon McWeeney OHSU Knight Cancer Institute Portland OR 97239 USA. OHSU School of Dentistry Portland OR 97239 USA. Michigan State University College of Osteopathic Medicine East Lansing USA. Program in Biomedical Sciences Anesthesiology and Perioperative Medicine School of Medicine Oregon Health & Science University Portland OR 97239 USA. Recursion Pharmaceuticals Salt Lake City UT USA. Department of Dermatology Oregon Health & Science University Portland OR 97239 USA. Fred Hutch Cancer Center Seattle WA USA. Randall Children's Cancer and Blood Disorders Program at Legacy Emmanuel Portland OR USA. Department of Cell Developmental and Cancer Biology Oregon Health & Science University Portland OR 97239 USA. OHSU Knight Cancer Institute Portland OR 97239 USA. mcweeney@ohsu.edu. Division of Bioinformatics and Computational Biology Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland OR 97239 USA. mcweeney@ohsu.edu. Division of Oncological Sciences Oregon Health & Science University Portland OR 97239 USA. mcweeney@ohsu.edu.

Even though head and neck squamous cell carcinoma (HNSCC) is the seventh most common cancer worldwide, there are only two PD-1 targeted immunotherapies (pembrolizumab and nivolumab) and one tumor intrinsic EGFR targeted therapy (cetuximab) that are FDA approved for treatment of HNSCC. Taking advantage of a high throughput inhibitor assay and computational tools originally showing success in leukemia, we designed and employed HNSCC-specific inhibitor panels that capture the diversity of aberrational pathways in HNSCC to test viable cells derived from patients' HNSCC tumors. This provides a functional context to the multi-omic readouts conducted on these samples (mutations, protein expression and copy number alterations). In addition to generating these deeply characterized functional genomics datasets, we also developed additional visual analytics that have the potential to provide greater insight into HNSCC drug response patterns and potentially aid precision oncology tumor boards in evaluation and assessment of effective targeted therapeutic agents.

关键词： Antitumor drug screening assays HNSCC Multi-Omics

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Compressed diffusion

arXiv

引用

arXiv 2019年

作者： Gigante, Scott Stanley, Jay S. Vu, Ngan van Dijk, David Moon, Kevin R. Wolf, Guy Krishnaswamy, Smita Computational Biology and Bioinformatics Program Department of Computer Science Department of Genetics Yale University New HavenCT United States Department of Mathematics and Statistics Utah State University LoganUT United States Department of Mathematics and Statistics Université de Montréal MontéalQC Canada

Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most kernel methods, its implementation requires a heavy computational load, reaching up to cubic complexity in the number of data points. This limits its usability in modern data analysis. Here, we present a new approach to computing the diffusion geometry, and related embeddings, from a compressed diffusion process between data regions rather than data points. Our construction is based on an adaptation of the previously proposed measure-based Gaussian correlation (MGC) kernel that robustly captures the local geometry around data points. We use this MGC kernel to efficiently compress diffusion relations from pointwise to data region resolution. Finally, a spectral embedding of the data regions provides coordinates that are used to interpolate and approximate the pointwise diffusion map embedding of data. We analyze theoretical connections between our construction and the original diffusion geometry of diffusion maps, and demonstrate the utility of our method in analyzing big datasets, where it outperforms competing approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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