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An Open-Source Knowledge Graph Ecosystem for the Life sciences

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

Indirect identification of horizontal gene transfer

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arXiv 2020年

作者： Schaller, David Lafond, Manuel Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Department of Computer Science Université de Sherbrooke 2500 boul. de l’Université SherbrookeQCJ1K 2R1 Canada Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs. © 2020, CC BY.

关键词： Phylogenetic

GREGoR: Accelerating Genomics for Rare Diseases

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arXiv 2024年

作者： Dawood, Moez Heavner, Ben Wheeler, Marsha M. Ungar, Rachel A. LoTempio, Jonathan Wiel, Laurens Berger, Seth Bernstein, Jonathan A. Chong, Jessica X. Délot, Emmanuèle C. Eichler, Evan E. Gibbs, Richard A. Lupski, James R. Shojaie, Ali Talkowski, Michael E. Wagner, Alex H. Wei, Chia-Lin Wellington, Christopher Wheeler, Matthew T. Carvalho, Claudia M.B. Gifford, Casey A. May, Susanne Miller, Danny E. Rehm, Heidi L. Sedlazeck, Fritz J. Vilain, Eric O'Donnell-Luria, Anne Posey, Jennifer E. Chadwick, Lisa H. Bamshad, Michael J. Montgomery, Stephen B. Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Biostatistics University of Washington SeattleWA United States Department of Genetics School of Medicine Stanford University StanfordCA United States Department of Pathology School of Medicine Stanford University Stanford CA United States Stanford Center for Biomedical Ethics School of Medicine Stanford University Stanford CA United States Institute for Clinical and Translational Science University of California IrvineCA United States Division of Cardiovascular Medicine School of Medicine Stanford University Stanford CA United States Division of Genetics and Metabolism Children’s National Rare Disease Institute WashingtonDC United States Center for Genetic Medicine Research Children’s National Rare Disease Institute WashingtonDC United States Department of Genomics and Precision Medicine George Washington University WashingtonDC United States Department of Pediatrics School of Medicine Stanford University Stanford CA United States Department of Pediatrics Dvision of Genetic Medicine University of Washington SeattleWA United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Howard Hughes Medical Institute University of Washington SeattleWA United States Department of Pediatrics Baylor College of Medicine HoustonTX United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard BostonMA United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Stanley Center for

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic data science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://***/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics. © 2024, CC BY.

关键词： Genome

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

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Journal of proteome research 2023年第4期22卷 1024-1042页

作者： Gilbert S Omenn Lydie Lane Christopher M Overall Charles Pineau Nicolle H Packer Ileana M Cristea Cecilia Lindskog Susan T Weintraub Sandra Orchard Michael H A Roehrl Edouard Nice Siqi Liu Nuno Bandeira Yu-Ju Chen Tiannan Guo Ruedi Aebersold Robert L Moritz Eric W Deutsch University of Michigan Ann Arbor Michigan48109 United States. Institute for Systems Biology Seattle Washington98109 United States. CALIPHO Group SIB Swiss Institute of Bioinformatics and University of Geneva 1015Lausanne Switzerland. University of British Columbia Vancouver British ColumbiaV6T 1Z4 Canada. French Institute of Health and Medical Research 35042RENNESCedexFrance. Macquarie University Sydney New South Wales2109 Australia. Griffith University's Institute for Glycomics Sydney New South Wales2109 Australia. Princeton University Princeton New Jersey08544 United States. Uppsala University 752 36Uppsala Sweden. University of Texas Health Science Center-San Antonio San Antonio Texas78229-3900 United States. EMBL-EBI Hinxton CambridgeshireCB10 1SD United Kingdom. Memorial Sloan Kettering Cancer Center New York New York10065 United States. Monash University ClaytonVictoria3800 Australia. BGI Group Shenzhen518083 P. R. China. University of California San Diego La Jolla California92093 United States. National Taiwan University Academia Sinica Nankang Taipei11529 Taiwan. Westlake University Guomics Laboratory of Big Proteomic Data Hangzhou310024 ZhejiangProvinceP. R. China. Institute of Molecular Systems Biology in ETH Zurich 8092Zurich Switzerland.

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

关键词： Biology and Disease-HPP (B/D-HPP) Grand Challenge Project Human Protein Atlas Human Proteome Project (HPP) Mass Spectrometry Interactive Virtual Environment (MassIVE) PeptideAtlas Ribo-Seq chromosome-centric HPP (C-HPP) missing proteins (MP) neXtProt protein existence (PE metrics) non-MS PE1 proteins small open reading frames (smORFs) uncharacterized protein existence 1 (uPE1)

A New Method Based on Evolutionary Algorithm for Symbolic Network Weak Unbalance

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Journal on Internet of Things 2019年第2期1卷 41-53页

作者： Yirong Jiang Weijin Jiang Jiahui Chen Yang Wang Yuhui Xu Lina Tan Liang Guo Tonghua Normal University Tonghua134002China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of CommerceChangsha410205China Institute of Big Data and Internet Innovation Hunan University of CommerceChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China School of Bioinformatics University of MinnesotaTwin CitiesUSA

The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many *** unbalance is an important indicator to measure the network *** paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary *** on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed *** EAWSB,this paper proposes a compression-based indirect representation method,which effectively reduces the size of the genotype space,thus making the algorithm search more complete and easier to get better solutions.

关键词： Weak structural balance signed networks evolutionary algorithms incremental computation compressed representation

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

Author Correction: Predicting the efficiency of prime editing guide RNAs in human cells

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Nature biotechnology 2024年第3期42卷 529页

作者： Hui Kwon Kim Goosang Yu Jinman Park Seonwoo Min Sungtae Lee Sungroh Yoon Hyongbum Henry Kim Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Graduate School of Data Science Seoul National University Seoul Republic of Korea. Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac. Severance Biomedical Science Institute Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of NanoScience and Technology Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac.

Longitudinal Proteogenomic Analysis In High-Grade Serous Ovarian Cancer Patients Addressed To Neoadjuvant Chemotherapy Followed By Interval Debulking Surgery

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Valentina Iacobelli Marianna Buttarelli Alessia Piermattei Enrica Martinelli Giuseppina Raspaglio Marta De Donato Francesca Sillano Rita Trozzi Tina Pasciuto Maria De Bonis Angelo Minucci Luca Mastrantoni Luciano Giaco' Giulia Mantini Eduardo Maria Sommella Vicky Caponigro Pietro Campiglia Gloria Anderson Giovanni Scambia Camilla Nero Department of Women's and Children's Health Sciences and Public Health UOC Gynecologic Oncology Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Department of Life Sciences and Public Health Section of Gynecology and Obstetrics Catholic University of the Sacred Heart Rome Italy Gynecopathology and Breast Pathology Unit Department of Woman and Child's Health and Public Health Sciences Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Data Collection Research Core Facilty Gemelli Science and Technology Park (GSTeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Section of Hygiene University Department of Life Sciences and Public Health Università Cattolica Del Sacro Cuore Rome Italy Molecular and Genomic Diagnostics Unit Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy UOC Medical Oncology Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Bioinformatics Research Core Facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Department of Pharmacy University of Salerno Salerno Italy

Optimized memory reveals a survival strategy for a multi-agent competition

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arXiv 2019年

作者： Khalighi, Moein Ardalankia, Jamshid Rizi, Abbas Karimi Ebadi, Haleh Jafari, Gholamreza Physics Department Shahid Beheshti University G.C. Evin Tehran19839 Iran Department of Financial Management Shahid Beheshti University G.C. Evin Tehran19839 Iran Bioinformatics Institute for Computer Science Leipzig University Härtelstrasse 16-18 Leipzig04107 Germany Department of Network and Data Science Central European University Budapest1051 Hungary

We present a fractional model to clarify the dynamical evolution of how and under what circumstances-in a multi-agent economic area-newly founded ventures prolong their existence throughout the market. Since the increase in the number of newly-established firms in a market may generally lead to the reduction of the market share of players in the contest, after a while some may be faded out in the market. In this regard, considering a long term evolution, all firms are exposed to be eliminated and to give up their market share. That is why managers have the concern of attracting more customers to satisfy at least a minimum market share, and ideally prolong their firm's lifetime. On the other side, due to the establishment of more newly founded ventures in a certain industry, it is vital to be flexible and run new strategies. The triggered strategies not only must be on time but also must be led to lengthening the additional survival time. In the present study, it is shown that the existence of memory or lack of memory in the evolution may not be the case, but also having a strategy plays a significant role in survival. Hence, in spite of exposure to the risk of missing market share, the recalling of the past offered services and products of the weaker firm-compared with the whole market-may prolong the time-length of surviving in the market. In this regard, managers corresponding to the weaker firm will be able to quantitatively make decisions toward two main concepts, firstly the strategical usage of their firms' reputation and secondly, launching new features and services. The aim behind this optimization is to delay the time of reaching a minimum market share. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems