检索结果-内蒙古大学图书馆

Machine learning approaches enable the discovery of therapeutics across domains

Molecular Therapy 2025年第5期33卷 2269-2278页

作者： Chhibbar, Prabal Das, Jishnu Centre for Systems Immunology Department of Immunology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Department of Computational and Systems Biology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Integrative Systems Biology PhD Program School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States

Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular, and humoral profiles. Corresponding inference of mechanisms can help to uncover new therapeutic targets. Here, we discuss how biological principles guide the design of predictive models and how interpretable machine learning can lead to novel mechanistic insights. We provide descriptions of multiple learning techniques and how suited they are to domain adaptations. Finally, we talk about broad learning capabilities of foundation models on large datasets and whether they can be used to provide meaningful inference about biological datasets. © 2025 The Author(s)

关键词： deep learning foundation models interpretable machine learning therapeutics

来源：评论

学校读者我要写书评

暂无评论

Protein codes promote selective subcellular compartmentalization

引用

Science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Regulatory T cells constrain T cells of shared specificity to enforce tolerance during infection

引用

Science (New York, N.Y.) 2025年第6740期387卷 eadk3248页

作者： Klawon, David E J Pagane, Nicole Walker, Matthew T. Ganci, Nicole K. Miller, Christine H. Gai, Eric Rodriguez, Donald M. Ryan-Payseur, Bridgett K. Duncombe, Ryan K. Adams, Erin J. Maienschein-Cline, Mark Freitag, Nancy E. Germain, Ronald N. Wong, Harikesh S. Savage, Peter A. Department of Pathology University of Chicago Chicago IL United States Ragon Institute of Mass General MIT and Harvard Cambridge MA United States Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA United States Interdisciplinary Scientist Training Program University of Chicago Chicago IL United States Department of Microbiology and Immunology University of Illinois Chicago Chicago IL United States Department of Biochemistry and Molecular Biology University of Chicago Chicago IL United States Research Informatics Core Research Resources Center University of Illinois Chicago Chicago IL United States Department of Pharmaceutical Sciences University of Illinois Chicago Chicago IL United States Lymphocyte Biology Section Laboratory of Immune System Biology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD United States Department of Biology Massachusetts Institute of Technology Cambridge MA United States

During infections, CD4+ Foxp3+ regulatory T (Treg) cells must control autoreactive CD4+ conventional T (Tconv) cell responses against self-peptide antigens while permitting those against pathogen-derived "nonself" peptides. We defined the basis of this selectivity using mice in which Treg cells reactive to a single prostate-specific self-peptide were selectively depleted. We found that self-peptide-specific Treg cells were dispensable for the control of Tconv cells of matched specificity at homeostasis. However, they were required to control such Tconv cells and prevent autoimmunity toward the prostate after exposure to elevated self-peptide during infection. Notably, the Treg cell response to self-peptide did not affect protective Tconv cell responses to a pathogen-derived peptide. Thus, self-peptide-specific Treg cells promoted self-nonself discrimination during infection by selectively controlling Tconv cells of shared self-specificity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dish-Trend: Intervention Modeling Simulator That Accounts for Trend Influences

Dish-Trend: Intervention Modeling Simulator That Accounts fo...

引用

2021 Winter Simulation Conference, WSC 2021

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781665433112

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend-and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. © 2021 IEEE.

关键词： Directed graphs

来源：评论

学校读者我要写书评

暂无评论

Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences

arXiv

引用

arXiv 2025年

作者： Ramesh, Krithik Siddiqui, Sameed M. Gu, Albert mitzenmacher, Michael D. Sabeti, Pardis C. Broad Institute of MIT and Harvard CambridgeMA02142 United States Massachusetts Institute of Technology CambridgeMA02139 United States Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States Machine Learning Department Carnegie Mellon University PittsburgPA15213 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Organismic and Evolutionary Biology Harvard University CambridgeMA02138 United States Department of Immunology and Infectious Diseases Harvard T.H. Chan School of Public Health Harvard University BostonMA02115 United States Howard Hughes Medical Institute Chevy ChaseMD20815 United States

Deep learning architectures such as convolutional neural networks and Transformers have revolutionized biological sequence modeling, with recent advances driven by scaling up foundation and task-specific models. The computational resources and large datasets required, however, limit their applicability in biological contexts. We introduce Lyra, a subquadratic architecture for sequence modeling, grounded in the biological framework of epistasis for understanding sequence-to-function relationships. Mathematically, we demonstrate that state space models efficiently capture global epistatic interactions and combine them with projected gated convolutions for modeling local relationships. We demonstrate that Lyra is performant across over 100 wide-ranging biological tasks, achieving state-of-the-art (SOTA) performance in many key areas, including protein fitness landscape prediction, biophysical property prediction (e.g. disordered protein region functions) peptide engineering applications (e.g. antibody binding, cell-penetrating peptide prediction), RNA structure analysis, RNA function prediction, and CRISPR guide design. It achieves this with orders-of-magnitude improvements in inference speed and reduction in parameters (up to 120,000-fold in our tests) compared to recent biology foundation models. Using Lyra, we were able to train and run every task in this study on two or fewer GPUs in under two hours, democratizing access to biological sequence modeling at SOTA performance, with potential applications to many fields. © 2025, CC BY-NC-ND.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Prediction of protein subcellular localization in single cells

引用

Nature methods 2025年 2025 May 13页

作者： Xinyi Zhang Yitong Tseo Yunhao Bai Fei Chen Caroline Uhler Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA. Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge MA USA. Computational and Systems Biology Program Massachusetts Institute of Technology Cambridge MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. chenf@***. Department of Stem Cell and Regenerative Biology Harvard University Cambridge MA USA. chenf@***. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA. cuhler@mit.edu. Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge MA USA. cuhler@mit.edu.

The subcellular localization of a protein is important for its function, and its mislocalization is linked to numerous diseases. Existing datasets capture limited pairs of proteins and cell lines, and existing protein localization prediction models either miss cell-type specificity or cannot generalize to unseen proteins. Here we present a method for Prediction of Unseen Proteins' Subcellular localization (PUPS). PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images. We demonstrate that the protein sequence input enables generalization to unseen proteins, and the cellular image input captures single-cell variability, enabling cell-type-specific predictions. Experimental validation shows that PUPS can predict protein localization in newly performed experiments outside the Human Protein Atlas used for training. Collectively, PUPS provides a framework for predicting differential protein localization across cell lines and single cells within a cell line, including changes in protein localization driven by mutations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons

引用

Neurobiology of Disease 2025年 210卷 106914-106914页

作者： Stocksdale, Jennifer T. Leventhal, Matthew J. Lam, Stephanie Xu, Yu-Xin Wang, Yang Oliver Wang, Keona Q. Thomas, Reuben Faghihmonzavi, Zohreh Raghav, Yogindra Smith, Charlene Wu, Jie Miramontes, Ricardo Sarda, Kanchan Johnston, Heather Shin, Min-Gyoung Huang, Terry Foster, Mikelle Barch, Mariya Amirani, Naufa Paiz, Chris Easter, Lindsay Duderstadt, Erse Vaibhav, Vineet Sundararaman, Niveda Felsenfeld, Dan P. Vogt, Thomas F. Van Eyk, Jennifer Finkbeiner, Steve Kaye, Julia A. Fraenkel, Ernest Thompson, Leslie M. Department of Neurobiology and Behavior UC Irvine Irvine 92677 CA United States MIT PhD Program in Computational and Systems Biology Cambridge 02139 MA United States MIT Department of Biological Engineering Cambridge 02139 MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge 02139 MA United States Center for Systems and Therapeutics Gladstone Institutes San Francisco 94158 CA United States Advanced Clinical Biosystems Research Institute Smidt Heart Institute Cedars-Sinai Medical Center Los Angeles 90048 CA United States Institute of Data Science and Biotechnology Gladstone Institutes San Francisco 94158 CA United States Department of Psychiatry and Human Behavior UC Irvine Irvine 92697 CA United States Department of Biological Chemistry UC Irvine Irvine 92697 CA United States Institute for Memory Impairments and Neurological Disorders UC Irvine Irvine 92697 CA United States CHDI Management Inc New York 10001 NY United States Department of Physiology University of California San Francisco San Francisco 94158 CA United States Department of Neurology University of California San Francisco San Francisco 94158 CA United States Taube/Koret Center for Neurodegenerative Disease Research Gladstone Institutes San Francisco 94158 CA United States

Huntington's Disease (HD) is caused by a CAG repeat expansion in the gene encoding huntingtin (HTT). While normal HTT function appears impacted by the mutation, the specific pathways unique to CAG repeat expansion versus loss of normal function are unclear. To understand the impact of the CAG repeat expansion, we evaluated biological signatures of HTT knockout (HTT KO) versus those that occur from the CAG repeat expansion by applying multi-omics, live cell imaging, survival analysis and a novel feature-based pipeline to study cortical neurons (eCNs) derived from an isogenic human embryonic stem cell series (RUES2). HTT KO and the CAG repeat expansion influence developmental trajectories of eCNs, with opposing effects on growth. Network analyses of differentially expressed genes and proteins associated with enriched epigenetic motifs identified subnetworks common to CAG repeat expansion and HTT KO that include neuronal differentiation, cell cycle regulation, and mechanisms related to transcriptional repression, and may represent gain-of-function mechanisms that cannot be explained by HTT loss of function alone. A combination of dominant and loss-of-function mechanisms are likely involved in the aberrant neurodevelopmental and neurodegenerative features of HD that can help inform therapeutic strategies. © 2025

关键词： Embryonic stem cells Features Huntington's disease Machine learning Multi-omics Network analysis Robotic microscopy RUES2

来源：评论

学校读者我要写书评

暂无评论

Rewriting regulatory DNA to dissect and reprogram gene expression

引用

Cell 2025年

作者： Martyn, Gabriella E. Montgomery, Michael T. Jones, Hank Guo, Katherine Doughty, Benjamin R. Linder, Johannes Bisht, Deepa Xia, Fan Cai, Xiangmeng S. Chen, Ziwei Cochran, Kelly Lawrence, Kathryn A. Munson, Glen Pampari, Anusri Fulco, Charles P. Sahni, Nidhi Kelley, David R. Lander, Eric S. Kundaje, Anshul Engreitz, Jesse M. Department of Genetics Stanford University School of Medicine Stanford 94305 CA United States Basic Science and Engineering Initiative Stanford Children's Health Betty Irene Moore Children's Heart Center Stanford 94305 CA United States Calico Life Sciences LLC South San Francisco 94080 CA United States Department of Genitourinary Medical Oncology Division of Cancer Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Epigenetics and Molecular Carcinogenesis The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Computer Science Stanford University Stanford 94305 CA United States Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge 02142 MA United States Gene Regulation Observatory Broad Institute of MIT and Harvard Cambridge 02142 MA United States Broad Institute of MIT and Harvard Cambridge 02142 MA United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston 77230 TX United States Quantitative and Computational Biosciences Program Baylor College of Medicine Houston 77030 TX United States Department of Biology MIT Cambridge 02139 MA United States Department of Systems Biology Harvard Medical School Boston 02115 MA United States Stanford Cardiovascular Institute Stanford University Stanford 94305 CA United States

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context. © 2025 Elsevier Inc.

关键词： CRISPR enhancers gene regulation high-throughput screening non-coding variants predictive models prime editing RNA FlowFISH sequence design transcription factors

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Progressive plasticity during colorectal cancer metastasis

引用

Nature 2025年第8047期637卷 E28页

作者： Andrew Moorman Elizabeth K Benitez Francesco Cambuli Qingwen Jiang Ahmed Mahmoud Melissa Lumish Saskia Hartner Sasha Balkaran Jonathan Bermeo Simran Asawa Canan Firat Asha Saxena Fan Wu Anisha Luthra Cassandra Burdziak Yubin Xie Valeria Sgambati Kathleen Luckett Yanyun Li Zhifan Yi Ignas Masilionis Kevin Soares Emmanouil Pappou Rona Yaeger T Peter Kingham William Jarnagin Philip B Paty Martin R Weiser Linas Mazutis Michael D'Angelica Jinru Shia Julio Garcia-Aguilar Tal Nawy Travis J Hollmann Ronan Chaligné Francisco Sanchez-Vega Roshan Sharma Dana Pe'er Karuna Ganesh Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY USA. Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New York NY USA. New York Genome Center New York NY USA. Pharmacology Program Weill Cornell Graduate School New York NY USA. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Case Western Reserve University Cleveland OH USA. Department of Pathology and Laboratory Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Department of Surgery Memorial Sloan Kettering Cancer Center New York NY USA. Tri-Institutional PhD Program in Computational Biology and Medicine New York NY USA. Bristol Myers Squibb Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. peerd@***. Howard Hughes Medical Institute Chevy Chase MD USA. peerd@***. Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New York NY USA. ganeshk@***. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. ganeshk@***.

来源：评论

学校读者我要写书评

暂无评论

Spectral clustering strategies for heterogeneous disease expression data 18

Spectral clustering strategies for heterogeneous disease exp...

引用

18th Pacific Symposium on Biocomputing, PSB 2013

作者： Huang, Grace T. Cunningham, Kathryn I. Benos, Panayiotis V. Chennubhotla, Chakra S. Department of Computational and Systems Biology University of Pittsburgh PittsburghPA United States Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States Department of Computer Science University of Arizona TucsonAZ United States

Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping;all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：