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arXiv 2019年

作者： Afzal, Mohammad Atif Faiz Hachmann, Johannes Schrödinger Inc. PortlandOR97204 United States Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo The State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2023年 9卷 e45312页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods: We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001

关键词： artificial intelligence chatbot ChatGPT conversational agent education technology generative pre-trained transformer GPT machine learning medical education MedQA natural language processing NLP

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Improved Prediction Of Ligand-Protein Binding Affinities By Meta-Modeling

arXiv

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arXiv 2023年

作者： Lee, Ho-Joon Emani, Prashant S. Gerstein, Mark B. Dept. of Genetics and Yale University New HavenCT06510 United States Yale Center for Genome Analysis Yale University New HavenCT06510 United States Dept. of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Dept. of Computer Science Yale University New HavenCT06520 United States Dept. of Statistics & Data Science Yale University New HavenCT06520 United States Dept. of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures, while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction. © 2023, CC BY-NC-SA.

关键词： Prediction models

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Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory

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Nature communications 2025年第1期16卷 4450页

作者： Suraj Rajendran Zhenxing Xu Weishen Pan Chengxi Zang Ilias Siempos Lisa Torres Jie Xu Jiang Bian Edward J Schenck Fei Wang Tri-Institutional Computational Biology & Medicine Program Cornell University Cornell NY USA. Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Division of Pulmonary and Critical Care Medicine Department of Medicine New York-Presbyterian Hospital-Weill Cornell Medical Center Weill Cornell Medicine New York NY USA. First Department of Critical Care Medicine and Pulmonary Services Evangelismos Hospital National and Kapodistrian University of Athens Medical School Athens Greece. Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Regenstrief Institute Indianapolis IN USA. Division of Pulmonary and Critical Care Medicine Department of Medicine New York-Presbyterian Hospital-Weill Cornell Medical Center Weill Cornell Medicine New York NY USA. ejs9005@med.cornell.edu. Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New York NY USA. few2001@med.cornell.edu.

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefit of corticosteroids are not fully defined using the construct of organ dysfunction duration. This retrospective multicenter, proof-of-concept study aimed to evaluate the association between usage of corticosteroids and mortality of patients with sepsis, pneumonia and ARDS by emulating a target trial framework stratified by predicted organ dysfunction trajectory. The study employed a two staged machine learning (ML) methodology to first subphenotype based on organ dysfunction trajectory then predict this defined trajectory. Once patients were classified by predicted trajectory we conducted a target trial emulation. Our analysis revealed that the association between corticosteroid use and 28-day mortality varied by predicted trajectory and between *** findings suggest that matching treatment strategies to empirically observed pathobiology may offer a more nuanced understanding of corticosteroid utility.

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Correction: Unbiased and multilevel methods for a class of diffusions partially observed via marked point processes

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Statistics and Computing 2024年第6期34卷 1-1页

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

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Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2024年 10卷 e57594页

In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions were made to enhance discoverability. The title originally appeared as: How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment And has been changed to: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge In the “Objective” section of the Abstract, the following sentence: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Has been changed to read as: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Finally, the abbreviation “USMLE” has been added to the Keywords section. The correction will appear in the online version of the paper on the JMIR Publications website on February 27, 2024 together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories. © 2024 JMIR Publications Inc.. All rights reserved.

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Relationship between amino acid composition and gene expression in the mouse genome

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BMC Research Notes 2011年第1期4卷 20-20页

作者： Misawa, Kazuharu Kikuno, Reiko F. Research Program for Computational Science Fusion of Data and Analysis Research and Development Team RIKEN Minato-ku Tokyo 108-8639 4-6-1 Shirokane-dai Japan Chiba Industry Advancement Center Mihama-ku Chiba 261-7126 2-6 Nakase Japan Kazusa DNA Research Institute Kisarazu Chiba 292-0818 2-6-7 Kazusa-Kamatari Japan

Background. Codon bias is a phenomenon that refers to the differences in the frequencies of synonymous codons among different genes. In many organisms, natural selection is considered to be a cause of codon bias because codon usage in highly expressed genes is biased toward optimal codons. Methods have previously been developed to predict the expression level of genes from their nucleotide sequences, which is based on the observation that synonymous codon usage shows an overall bias toward a few codons called major codons. However, the relationship between codon bias and gene expression level, as proposed by the translation-selection model, is less evident in mammals. Findings. We investigated the correlations between the expression levels of 1,182 mouse genes and amino acid composition, as well as between gene expression and codon preference. We found that a weak but significant correlation exists between gene expression levels and amino acid composition in mouse. In total, less than 10% of variation of expression levels is explained by amino acid components. We found the effect of codon preference on gene expression was weaker than the effect of amino acid composition, because no significant correlations were observed with respect to codon preference. Conclusion. These results suggest that it is difficult to predict expression level from amino acid components or from codon bias in mouse. © 2011 Misawa et al;licensee BioMed Central Ltd.

关键词： Codon Gene Expression Level Amino Acid Composition Codon Usage Amino Acid Content

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P662: Estimating UK Biobank population-specific PGx allele and phenotype frequencies using PharmCAT

Genetics in Medicine Open

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Genetics in Medicine Open 2023年第1期1卷 100727-100727页

作者： Katrin Sangkuhl Ryan Whaley Mark Woon Karl Keat Li Gong Michelle Whirl-Carrillo Marylyn Ritchie Teri Klein Department of Biomedical Data Science Stanford University Stanford CA Genomics and Computational Biology PhD Program University of Pennsylvania Philadelphia PA Department of Genetics University of Pennsylvania Philadelphia PA

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A holistic exploration of the potentially recoverable redshift information of Stage IV galaxy surveys

arXiv

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arXiv 2024年

作者： Scott, Bryan R. Malz, Alex I. Sorba, Robert Northwestern University 1800 Sherman Ave 8th Floor EvanstonIL60201 United States LSST Discovery Alliance Data Science Fellowship Program United States McWilliams Center for Cosmology and Astrophysics Department of Physics Carnegie Mellon University United States Institute for Computational Astrophysics Saint Mary’s University 923 Robie Street HalifaxNSB3H 3C3 Canada

Extragalactic science and cosmology with Stage IV galaxy surveys will rely almost exclusively on redshift measurements derived solely from photometry, which are subject to systematic and statistical uncertainties with numerous analysis choices, such as that of an estimator and prior information and no universal solution. Single-survey photometric redshift estimates ought to be improved by combining data from multiple surveys, with common wisdom asserting that optical data benefits from additional infrared coverage but not from additional ultraviolet coverage. The degree of improvement for either case is not well-characterized and attempts to quantify it necessitate assumptions of a chosen estimator and its prior information. We apply an information theoretic metric of potentially recoverable redshift information to assess the impact of multi-survey photometry combinations without assuming an estimator nor priors in the context of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) in the optical, Roman and Euclid Surveys in the infrared, and Cosmological Advanced Survey Telescope for Optical-UV Research (CASTOR) in the ultraviolet. We conclude that the addition of UV photometry can benefit redshift determination of certain galaxy populations, but that gain is tempered by their decreased chance of meeting detection criteria at higher wavelengths. We explore the spectral energy distribution (SEDs) of galaxies whose potentially recoverable redshift information is most impacted by additional photometry to provide a basis for future evaluation of which science cases may motivate which combinations. The holistic assessment approach we develop here is generic and may be applied to quantify the impact of combining photometric datasets, changing experimental design choices, including observing strategy optimization, and evaluating systematics mitigation procedures. © 2024, CC BY.

关键词： Cosmology

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