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Science 2025年第6740期387卷 1261页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies The reviewer is at the Harry Radzyner Law School The reviewer is at the Dina Recanati School of Medicine Reichman University Herzliya Israel The reviewer is at the Department of Biomedical Informatics and Data Science The reviewer is at the Program in Computational Biology and Bioinformatics The reviewer is at the Department of Computer Science Yale University New Haven CT USA.

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Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study

JMIR Aging

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JMIR Aging 2025年 8卷 e69504页

作者： Socrates, Vimig Wright, Donald S. Huang, Thomas Fereydooni, Soraya Dien, Christine Chi, Ling Albano, Jesse Patterson, Brian Kanaparthy, Naga Sasidhar Wright, Catherine X. Loza, Andrew Chartash, David Iscoe, Mark Taylor, Richard Andrew Department of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States Department of Emergency Medicine School of Medicine Yale University New Haven CT United States VA Connecticut Healthcare System US Department of Veterans Affairs West Haven CT United States Department of Pharmacy Yale New Haven Hospital New Haven CT United States BerbeeWalsh Department of Emergency Medicine University of Wisconsin–Madison Madison WI United States Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT United States School of Medicine University College Dublin Dublin Ireland Department of Biostatistics School of Public Health Yale University New Haven CT United States

Background: Polypharmacy, the concurrent use of multiple medications, is prevalent among older adults and associated with increased risks for adverse drug events including falls. Deprescribing, the systematic process of discontinuing potentially inappropriate medications, aims to mitigate these risks. However, the practical application of deprescribing criteria in emergency settings remains limited due to time constraints and criteria complexity. Objective: This study aims to evaluate the performance of a large language model (LLM)–based pipeline in identifying deprescribing opportunities for older emergency department (ED) patients with polypharmacy, using 3 different sets of criteria: Beers, Screening Tool of Older People’s Prescriptions, and Geriatric Emergency Medication Safety Recommendations. The study further evaluates LLM confidence calibration and its ability to improve recommendation performance. Methods: We conducted a retrospective cohort study of older adults presenting to an ED in a large academic medical center in the Northeast United States from January 2022 to March 2022. A random sample of 100 patients (712 total oral medications) was selected for detailed analysis. The LLM pipeline consisted of two steps: (1) filtering high-yield deprescribing criteria based on patients’ medication lists, and (2) applying these criteria using both structured and unstructured patient data to recommend deprescribing. Model performance was assessed by comparing model recommendations to those of trained medical students, with discrepancies adjudicated by board-certified ED physicians. Selective prediction, a method that allows a model to abstain from low-confidence predictions to improve overall reliability, was applied to assess the model’s confidence and decision-making thresholds. Results: The LLM was significantly more effective in identifying deprescribing criteria (positive predictive value: 0.83;negative predictive value: 0.93;McNemar test for paired proportion

关键词： calibration deprescribing emergency medicine geriatrics large language models natural language processing potentially inappropriate medication list

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Using dnase digestion data to accurately identify transcription factor binding sites 18

Using dnase digestion data to accurately identify transcript...

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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Luo, Kaixuan Hartemink, Alexander J. Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Computer Science Duke University DurhamNC27708 United States

Identifying binding sites of transcription factors (TFs) is a key task in deciphering transcriptional regulation. ChIP-based methods are used to survey the genomic locations of a single TF in each experiment. But methods combining DNase digestion data with TF binding specificity information could potentially be used to survey the locations of many TFs in the same experiment, provided such methods permit reasonable levels of sensitivity and specificity. Here, we present a simple such method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). Our method is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well. Because the number of parameters in our method is at least an order of magnitude smaller than centipede, we dub it millipede.

关键词： Transcription factors

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A reservoir of 'historical' antibiotic resistance genes in remote pristine Antarctic soils

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Microbiome 2018年第1期6卷 1-12页

作者： Van Goethem, Marc W. Pierneef, Rian Bezuidt, Oliver K.I. Van De Peer, Yves Cowan, Don A. Makhalanyane, Thulani P. Centre for Microbial Ecology and Genomics (CMEG) Department of Biochemistry Genetics and Microbiology University of Pretoria Natural Sciences Building 2 Lynnwood Road Pretoria 0028 South Africa Centre for Bioinformatics and Computational Biology Department of Biochemistry Genetics and Microbiology University of Pretoria Pretoria South Africa Department of Plant Biotechnology and Bioinformatics Ghent University Ghent 9052 Belgium Center for Plant Systems Biology VIB Ghent 9052 Belgium Bioinformatics Institute Ghent Ghent University Ghent 9052 Belgium

Background: Soil bacteria naturally produce antibiotics as a competitive mechanism, with a concomitant evolution, and exchange by horizontal gene transfer, of a range of antibiotic resistance mechanisms. Surveys of bacterial resistance elements in edaphic systems have originated primarily from human-impacted environments, with relatively little information from remote and pristine environments, where the resistomemay comprise the ancestral gene diversity. Methods: We used shotgun metagenomics to assess antibiotic resistance gene (ARG) distribution in 17 pristine and remote Antarctic surface soils within the undisturbed Mackay Glacier region. We also interrogated the phylogenetic placement of ARGs compared to environmental ARG sequences and tested for the presence of horizontal gene transfer elements flanking ARGs. Results: In total, 177 naturally occurring ARGs were identified, most of which encoded single or multi-drug efflux pumps. Resistance mechanisms for the inactivation of aminoglycosides, chloramphenicol and β-lactam antibiotics were also common. Gram-negative bacteria harboured most ARGs (71%), with fewer genes from Gram-positive Actinobacteria and Bacilli (Firmicutes) (9%), reflecting the taxonomic composition of the soils. Strikingly, the abundance of ARGs per sample had a strong, negative correlation with species richness (r = - 0.49, P < 0.05). This result, coupled with a lack of mobile genetic elements flanking ARGs, suggests that these genes are ancient acquisitions of horizontal transfer events. Conclusions: ARGs in these remote and uncontaminated soils most likely represent functional efficient historical genes that have since been vertically inherited over generations. The historical ARGs in these pristine environments carry a strong phylogenetic signal and form a monophyletic group relative to ARGs from other similar environments. © The Author(s). 2018.

关键词： Antarctica Antibiotic resistance genes Metagenomics Soil resistome

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The bourque distances for mutation trees of cancers 20

The bourque distances for mutation trees of cancers

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20th International Workshop on Algorithms in bioinformatics, WABI 2020

作者： Jahn, Katharina Beerenwinkel, Niko Zhang, Louxin Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland Department of Mathematics and Computational Biology Program National University of Singapore Singapore

ISBN: (纸本)9783959771610

Mutation trees are rooted trees of arbitrary node degree in which each node is labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular RobinsonFoulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients often contain different sets of mutation labels. Here, we generalize the RobinsonFoulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. A connection between the RobinsonFoulds distance and the nearest neighbor interchange distance is also presented. © Katharina Jahn, Niko Beerenwinkel, and Louxin Zhang;licensed under Creative Commons License CC-BY

关键词： Bourque distance Clonal trees Mutation trees Phylogenetic trees Robinson–Foulds distance Tree distance Tree metric

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MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

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Frontiers of Electrical and Electronic Engineering in China 2018年第1期6卷 40-55页

作者： Yu Zhang Sha Cao Jing Zhao Burair Alsaihati Qin Ma Chi Zhang Collecles of Computer Science and Technoloqy Jilin University Chanqchun 130012 China Department of Biostatistics Indiana University School of Medicine IN 46202 USA Center for Health Outcomes and Population Research Sanford Research Sioux Falls SD 57104 USA Computational Systems Biology La'b Department of'Biochemistry and Molecular Biology and institute of Bioinformatics University of Georgia GA 30602 USA Bioinformatics and Mathematical Biosciences Lab Department of Agronomy Horticulture and Plant Science South Dakota State University Brookings SD 57007 USA Center for Computational-Biology and Bioinformatics and Department of Medical and Molecular Genetics Indiana University School of Medicine IN 46202 USA

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.

关键词： gene co-expression network algorithm for large scale networks analysis statistical significance of gene co-expression Mutual Rank

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Distribution of horizontally transferred heavy metal resistance operons in recent outbreak bacteria

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Mobile genetic elements 2012年第2期2卷 96-100页

作者： Oleg Reva Oliver Bezuidt Bioinformatics and Computational Biology Unit Department of Biochemistry University of Pretoria Pretoria South Africa.

Mankind is confronted by the outbreaks of highly virulent and multi-drug resistant pathogens. The outbreak strains often belong to well-known diseases associated species such as Salmonella, Klebsiella and Mycobacterium, but even normally commensal and environmental microorganisms may suddenly acquire properties of virulent bacteria and cause nosocomial infections. The acquired virulence is often associated with lateral exchange of pathogenicity genomic islands containing drug and heavy metal resistance determinants. Metal ions are used by the immune system of macro-organisms against bactericidal agents. The ability to control heavy metal homeostasis is a factor that allows the survival of pathogenic microorganisms in macrophages. In this paper, we investigate the origin of heavy metal resistance operons in the recent outbreak strains and the possible routes which may lead to acquisitions of these genes by potentially new pathogens. We hypothesize that new outbreak microorganisms appear intermittently on an intersection of the non-specialized, genetically naïve strains of potential pathogens and virulence factor comprising vectors (plasmid and/or phages) newly generated in the environmental microflora. Global contamination of the environment and climate change may also have an effect toward the acceleration and appearance of new pathogens.

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Control of Fetal Hemoglobin Levels By NFI Transcription Factors

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BLOOD 2020年 136卷 54-54页

作者： Qin, Kunhua Huang, Peng Huang, Anran Feng, Ruopeng Mayuranathan, Thiyagaraj Keller, Cheryl A. Giardine, Belinda Hardison, Ross C. Weiss, Mitchell J. Shi, Junwei Blobel, Gerd A. Division of Hematology Children's Hospital of Philadelphia Philadelphia PA Department of Hematology St. Jude Children's Research Hospital Memphis TN Department of Biochemistry and Molecular Biology Center for Computational Biology & Bioinformatics The Pennsylvania State University University Park PA Department of Cancer Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA

Sickle cell disease and some forms of b-thalassemia are disorders, which can be improved by therapies that elevate HbF, the fetal form of hemoglobin. It is currently thought that in adult type erythroid cells, silencing of the fetal type b-globin-like genes HBG1 and HBG2 is accomplished predominantly by two major transcription factors: BCL11A and ZBTB7A. However, it is unknown whether there are additional transcription factors that contribute to the repression of the HBG1/2 genes. Here, using a DNA-binding domain focused CRISPR-Cas9 screening approach, we identified NFIA as a novel regulator of HbF expression. NFIA belongs to the NFI transcription factor family, which is composed of NFIA, NFIB, NFIC and NFIX. NFIA and NFIX are the predominantly expressed forms in human erythroid cells, and their expression is higher in adult erythroblast cells when compared to fetal erythroid cells. CRISPR-Cas9 mediated disruption of NFIA in an immortalized human umbilical cord blood-derived erythroid progenitor cell line 2 (HUDEP2) or CD34+ hematopoietic stem and progenitor cell-derived primary erythroblast cells led to a modest reactivation of HBG1/2 mRNA expression, whereas disruption of NFIX had little effect. However, combined NFIA and NFIX disruption produced a substantial increase in HBG1/2 expression, suggesting that these factors function in a partially redundant manner. ChIP-seq and RNA-seq studies showed that NFIA and NFIX have comparable chromatin binding and activity profiles in human erythroid cells. ChIP-seq failed to detect NFI protein occupancy at or near the HBG1/2 genes. However, given the known difficulty in detecting repressor molecules by ChIP at the silent HBG1/2 genes [Martyn et al., Nature Genetics 2018; Liu et al., Cell 2018], we tested a direct involvement of NFI proteins by disrupting putative NFI binding sites near the HBG1/2 genes. Perturbation of one such NFI motif residing upstream of the transcription start site in the HBG1/2 promoters both in HUDEP2

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Uncovering Hidden Assembly Artifacts: When Unitigs are not Safe and Bidirected Graphs are not Helpful (ABSTRACT) 26th

Uncovering Hidden Assembly Artifacts: When Unitigs are not S...

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26th International Conference on Research in computational Molecular biology, RECOMB 2022

作者： Rahman, Amatur Medvedev, Paul Department of Computer Science and Engineering Penn State University Park United States Department of Biochemistry and Molecular Biology Penn State University Park United States Center for Computational Biology and Bioinformatics Penn State University Park United States

Recent assemblies by the T2T and VGP consortia have achieved significant accuracy but required a tremendous amount of effort and resources. © 2022, The Author(s), under exclusive license to Springer Nature Switze... 详细信息

ISBN: (纸本)9783031047480

关键词： Computers

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