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arXiv 2018年

作者： Hamid, Md-Nafiz Friedberg, Iddo Bioinformatics and Computational Biology Program Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA United States

Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against out-of-distribution (OoD) antibiotic resistant/non-resistant genes. In this paper, we introduce a deep learning model trained with Stochastic Gradient Langevin Dynamics (SGLD) to classify antibiotic resistant genes. The model provides better uncertainty estimates when tested against OoD data compared to traditional optimization methods such as Adam. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Gene Co-expression Network analysis of Lung Squamous Cell Carcinoma data

arXiv

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arXiv 2018年

作者： Hamid, Md-Nafiz Bioinformatics and Computational Biology program Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA United States

We performed a gene co-expression analysis on Lung Squamous Cell Carcinoma data to find modules (groups) of genes that may highly impact the growth of these type of tumors. Additionally, we used cancer survival data to relate modules to prognostic significance in terms of survival time. Analysis on RNA-seq data revealed modules which are significant in gene enrichment analysis. Specifically, two genes - RFC4 and ECT2 - have been found to be significant in terms of survival time. We also performed a second gene co-expression analysis on a second dataset of microarray data, and many significant genes found in this analysis could also be found in the RNA-seq data implying that these genes might indeed play a crucial role in Lung Squamous Cancer. All the R code for the analysis can be found at: https://***/nafizh/Gene_ coexpression_analysis_lung_cancer Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Rank Projection Trees for Multilevel Neural Network Interpretation

arXiv

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arXiv 2018年

作者： Warrell, Jonathan Mohsen, Hussein Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University Molecular Biophysics and Biochemistry Yale University Department of Computer Science Yale University

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Perspective on recent developments and challenges in regulatory and systems genomics

arXiv

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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition (vol 9, 10205, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 1-11页

作者： Mahalapbutr, Panupong Darai, Nitchakan Panman, Wanwisa Opasmahakul, Aunchan Kungwan, Nawee Hannongbua, Supot Rungrotmongkol, Thanyada Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Program in Biotechnology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Department of Chemistry Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand Center of Excellence in Materials Science and Technology Chiang Mai University 50200 Chiang Mai Thailand Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Molecular Sensory Science Center Faculty of Science Chulalongkorn University 10330 Bangkok Thailand

The human T1R2-T1R3 sweet taste receptor (STR) plays an important role in recognizing various low-molecular-weight sweet-tasting sugars and proteins, resulting in the release of intracellular heterotrimeric G protein that in turn leads to the sweet taste perception. Xylitol and sorbitol, which are naturally occurring sugar alcohols (polyols) found in many fruits and vegetables, exhibit the potential caries-reducing effect and are widely used for diabetic patients as low-calorie sweeteners. In the present study, computational tools were applied to investigate the structural details of binary complexes formed between these two polyols and the T1R2-T1R3 heterodimeric STR. Principal component analysis revealed that the Venus flytrap domain (VFD) of T1R2 monomer was adapted by the induced-fit mechanism to accommodate the focused polyols, in which α-helical residues 233–268 moved significantly closer to stabilize ligands. This finding likely suggested that these structural transformations might be the important mechanisms underlying polyols-STR recognitions. The calculated free energies also supported the VFD of T1R2 monomer as the preferential binding site for such polyols, rather than T1R3 region, in accord with the lower number of accessible water molecules in the T1R2 pocket. The E302 amino acid residue in T1R2 was found to be the important recognition residue for polyols binding through a strongly formed hydrogen bond. Additionally, the binding affinity of xylitol toward the T1R2 monomer was significantly higher than that of sorbitol, making it a sweeter tasting molecule. [ABSTRACT FROM AUTHOR]

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

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Nature genetics 2023年第6期55卷 1078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. Samsung Genome Institute Samsung Medical Center 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. Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University School of Medicine Seoul South Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Department of Haematology University of Cambridge Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson.org. Quantitative and Comp

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Development of a quantitative structure-response relationships to estimate concentrations of plasticizer metabolites in urine without reference standards using non-targeted analysis with liquid chromatography high-resolution mass spectrometry

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Analytica chimica acta 2025年 1364卷 344215页

作者： Yong-Lai Feng Jerrie Feng Anca Baesu Vanessa Martinez Yan Li Exposure and Biomonitoring Division Environmental Health Science and Research Bureau Health Canada 251 Sir Frederick Banting Driveway Ottawa Ontario K1A 0K9 Canada. Electronic address: yong-lai.feng@hc-sc.gc.ca. Bioinformatics and Computational Biology Program Faculty of Arts & Science University of Toronto 25 Harbord Street Toronto Ontario M5S 3G5 Canada. Exposure and Biomonitoring Division Environmental Health Science and Research Bureau Health Canada 251 Sir Frederick Banting Driveway Ottawa Ontario K1A 0K9 Canada. Department of Chemistry and Biomolecular Sciences University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada.

BACKGROUND:Plasticizers are characterized as endocrine disruptors, and human exposure to these chemicals can adversely affect the reproductive system. Non-targeted analysis (NTA) using liquid chromatography - high resolution mass spectrometry (LC-HRMS) is a discovery-based approach for screening and identifying hundreds of compounds and metabolites in human samples without a priori knowledge of the chemical species that would be present in the sample. However, while NTA methods need to be standardized in order to provide reliable and reproducible qualitative results, there is also an urgent need to develop NTA methods that can provide quantitative information for identified compounds. RESULTS:We describe a quantitative structure-response relationship (QSRR) in NTA using LC-electrospray ionization (ESI)-HRMS that was established by combining retention behavior and structural descriptors. This relationship can be used to estimate the signal response factors of chemicals and we can utilize the estimated response factors to approximate the concentrations of plasticizer metabolites without reference standards. The model consists of 12 variables, being retention time and 11 molecular descriptors, that can estimate response factors of eleven testing chemicals with an error less than 30 %. The study demonstrated that the model can be updated with routine (e.g. daily) calibration curves and estimate concentrations of unknown metabolites in urine with an error less than 50 % for most compounds. The model can be applied to two different ionization systems without having a significant impact on its prediction accuracy. SIGNIFICANCE:While NTA provides the important information on identification of unknown and new chemicals/metabolites, it is crucial to provide quantitative concentrations for these compounds identified in NTA to support exposure assessment. Our approach enables a fast and simple estimation of the concentration of unknown metabolites of phthalates and their altern

关键词： Liquid chromatography

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Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Research Square

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Research Square 2021年

作者： Freedman, Matthew L. Baca, Sylvan C. Singler, Cassandra Zacharia, Soumya Seo, Ji-Heui Morova, Tunc Hach, Faraz Ding, Yi Schwarz, Tommer Huang, Chia-Chi Flora Kalita, Cynthia Groha, Stefan Pomerantz, Mark M. Wang, Victoria Linder, Simon Sweeney, Christopher J. Zwart, Wilbert Lack, Nathan A. Pasaniuc, Bogdan Takeda, David Y. Gusev, Alexander Department of Medical Oncology Dana-Farber Cancer Institute BostonMA United States Center for Functional Cancer Epigenetics Dana-Farber Cancer Institute BostonMA United States The Eli and Edythe L. Broad Institute CambridgeMA United States Laboratory of Genitourinary Cancer Pathogenesis Center for Cancer Research National Cancer Institute BethesdaMD United States Vancouver Prostate Centre University of British Columbia VancouverBC Canada Bioinformatics Interdepartmental Program UCLA Los AngelesCA United States Division of Genetics Brigham & Women’s Hospital BostonMA United States Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute BostonMA United States Department of Biostatistics Harvard School of Public Health BostonMA United States Division of Oncogenomics Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Laboratory of Chemical Biology Institute for Complex Molecular Systems Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Medicine Koç University Istanbul Turkey Department of Computational Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation. © 2021, CC BY.

关键词： Transcription

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Detection of emergency department patients at risk of dementia through artificial intelligence

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷 e70334页

作者： Inessa Cohen Richard Andrew Taylor Haipeng Xue Isaac V Faustino Natalia Festa Cynthia Brandt Emily Gao Ling Han Siddarth Khasnavis James M Lai Adam P Mecca Atharva Vinay Sapre Juan Young Michael Zanchelli Ula Hwang Department of Emergency Medicine Yale School of Medicine New Haven Connecticut USA. Department of Biomedical Informatics and Data Science Yale University School of Medicine New Haven Connecticut USA. Program of Computational Biology and Bioinformatics Yale University School of Medicine New Haven Connecticut USA. Department of Internal Medicine Yale School of Medicine New Haven Connecticut USA. Department of Psychiatry Yale School of Medicine New Haven Connecticut USA. Division of Geriatric Medicine and Palliative Care Department of Internal Medicine New York University Grossman School of Medicine New York New York USA. Geriatric Research Education and Clinical Center James J. Peters VAMC Bronx New York USA. Departments of Emergency Medicine and Population Health New York University Grossman School of Medicine New York New York USA.

INTRODUCTION:The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED). METHODS:In a multisite retrospective study of 759,665 ED visits, electronic health record data from Yale New Haven Health (2014-2022) were used to train three supervised and semi-unsupervised positive-unlabeled machine learning models (XGBoost, Random Forest, LASSO). A separate test set of 400 ED encounters underwent adjudicated chart review for validation. RESULTS:EDDA achieved an area under the receiver-operating characteristic curve (AUROC) of 0.85 in the test set and 0.93 in the validation set. Positive-unlabeled learning improved performance. Agreement between EDDA and clinician-adjudicated dementia diagnoses was moderate (kappa = 0.50), with 17% of EDDA-positive patients having undiagnosed probable dementia. DISCUSSION:EDDA enhances dementia detection in the ED, with potential for real-time implementation to improve patient outcomes and care transitions. HIGHLIGHTS:Developed a machine learning algorithm using electronic health record data to detect dementia in the emergency department (ED). Algorithm designed to balance detection accuracy with ease of ED implementation. Parsimonious model with limited but predictive variables selected for rapid ED use. Focused on real-time application, optimizing ED workflows, and clinician support. Aims to enhance ED dementia detection, patient safety, and care coordination.

关键词： care transition intervention cognitive impairment dementia early dementia detection emergency department

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Void distributions reveal structural link between jammed packings and protein cores

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Physical Review E 2019年第2期99卷 022416-022416页

作者： John D. Treado Zhe Mei Lynne Regan Corey S. O'Hern Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical & Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Chemistry Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction ϕ≈0.56, which is the same as dense, random packing of amino-acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino-acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. We measure the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation. We find that the cluster size statistics are similar for void percolation in packings of amino-acid-shaped particles and randomly placed spheres, but different from that for void percolation in jammed sphere packings. We propose that the connected void regions are a defining structural feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino-acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations.

关键词： Jamming Protein structure Packing & jamming problems Proteins Crystal structures Finite-size scaling Molecular dynamics Percolation theory

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