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arXiv 2024年

作者： Wang, Gaoyuan Warrell, Jonathan Emani, Prashant S. Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Computer Science Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States NEC Laboratories America PrincetonNJ08540 United States

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves compressing the original data to manageable dimensions while still representing essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To bridge this gap, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied for both classical and quantum data compression. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a"global"training objective. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results consistently indicate that ζ-QVAE exhibits similar or better performance compared to matched classical models. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Hopper flows of deformable particles

arXiv

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arXiv 2022年

作者： Cheng, Yuxuan Treado, John D. Lonial, Ben Habdas, Piotr Weeks, Eric R. Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States Department of Physics Saint Joseph's University PhiladelphiaPA19131 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Numerous experimental and computational studies show that continuous hopper flows of granular materials obey the Beverloo equation that relates the volume flow rate Q and the orifice width w: Q ∼ (w/σavg − k)β, where σavg is the average particle diameter, kσavg is an offset where Q ∼ 0, the power-law scaling exponent β = d − 1/2, and d is the spatial dimension. Recent studies of hopper flows of deformable particles in different background fluids suggest that the particle stiffness and dissipation mechanism can also strongly affect the power-law scaling exponent β. We carry out computational studies of hopper flows of deformable particles with both kinetic friction and background fluid dissipation in two and three dimensions. We show that the exponent β varies continuously with the ratio of the viscous drag to the kinetic friction coefficient, λ = ζ/µ. β = d−1/2 in the λ → 0 limit and d− 3/2 in the λ → ∞ limit, with a midpoint λc that depends on the hopper opening angle θw. We also characterize the spatial structure of the flows and associate changes in spatial structure of the hopper flows to changes in the exponent β. The offset k increases with particle stiffness until k ∼ kmax in the hard-particle limit, where kmax ∼ 3.5 is larger for λ → ∞ compared to that for λ → 0. Finally, we show that the simulations of hopper flows of deformable particles in the λ → ∞ limit recapitulate the experimental results for quasi-2D hopper flows of oil droplets in water. © 2022, CC BY.

关键词： Stiffness

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Cell2Sentence: Teaching Large Language Models the Language of biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

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Cell 2025年

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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Author Correction: Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

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Nature genetics 2024年第6期56卷 1319页

作者： Sneha Mitra Rohan Malik Wilfred Wong Afsana Rahman Alexander J Hartemink Yuri Pritykin Kushal K Dey Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. Rye Country Day School Rye NY USA. Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA. Hunter College City University of New York New York City NY USA. Department of Computer Science Duke University Durham NC USA. Program in Computational Biology and Bioinformatics Duke University Durham NC USA. Center for Genomic and Computational Biology Duke University Durham NC USA. Department of Computer Science Princeton University Princeton NJ USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. deyk@***. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. lesliec@***.

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Grappling with the Gulf

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Science 2009年第5911期323卷 210页

作者： Dov Greenbaum Mark Gerstein Mcdermott Will and Emory LLP 3150 Porter Drive Palo Alto CA 94304 USA. Department of Molecular Biophysics and Biochemistry Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT 06520 USA.

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A Comprehensive Survey of Human Proteome by Analyzing Large Scale Tandem Mass Spectrometry and RNA-sequencing Data

A Comprehensive Survey of Human Proteome by Analyzing Large ...

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第八届中国蛋白质组学大会

作者： Peng Li Jean Thierry-Mieg Baitang Ning James C Fuscoe Qi Lv Pengzhan Hu Ruichao Xue Christopher Mason Altuna Akalin Geng Chen Chen Zhao Weida Tong Huixiao Hong Lei Guo Hong Fang Tao Qing Ying Yu Wenzhong Xiao Danielle Thierry-Mieg Leming Shi Tieliu Shi Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China National Center for Biotechnology InformationNational Library of MedicineNIHBethesdaMD 20894USA National Center for Toxicological ResearchFood and Drug AdministrationJeffersonARUSA Department of PhysiologyBiophysicsand Systems BiologyTri-Institutional Program(CornellSloan-Ketteringand Rockefeller)on Medicine1305 York AveUSA Center for PharmacogenomicsSchool of PharmacyFudan UniversityShanghaiChina Massachusetts General HospitalHarvard Medical School51 Blossom StBostonMA 02114USA National Center for Toxicological ResearchFood and Drug AdministrationJeffersonARUSA Center for Pharmacogenomics School of PharmacyFudan UniversityShanghaiChina

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71 Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape

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Journal of Biomolecular Structure and Dynamics 2013年第SUPP 1期31卷 45-46页

作者： Raluca Gordan[a][b][*] Ning Shen[c] Iris Dror[d] Tianyin Zhou[d] John Horton[b] Remo Rohs[d] & Martha L. Bulyk[e][f] [a] Departments of Biostatistics and Bioinformatics Computer Science and Molecular Genetics and Microbiology Duke University Durham NC USA [b] Institute for Genome Sciences and Policy Duke University Durham NC 27708 USA Phone: Phone: +1 (919) 667-8673 Fax: Phone: +1 (919) 667-8673 [c] Department of Pharmacology and Cancer Biology Duke University Durham NC 27708 USA [d] Molecular and Computational Biology Program Departments of Biological Sciences Chemistry and Physics and Astronomy University of Southern California 1050 Childs Way Los Angeles CA 90089 USA [e] Division of Genetics Departments of Medicine and Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA 02115 USA [f] Harvard-MIT Division of Health Sciences and Technology (HST) Harvard Medical School Boston MA 02115 USA

Transcriptional regulation of gene expression is enacted mainly through binding of transcription factors (TFs) to specific, short DNA sites in cis-regulatory regions of genes. Most TFs are members of protein families that share a common DNA-binding domain and thus recognize similar DNA-binding sequences. It is not well understood why paralogous TFs often bind different genomic target sitesin vivoto effect different regulatory programs, despite apparently recognizing the same sequence motifs. Here, we designed custom protein-binding microarrays (PBMs) to analyze the DNA-binding specificities of twoSaccharomyces cerevisiaebasic helix-loop-helix (bHLH) proteins, Tye7 and Cbf1, as a model system. Our data reveal that E-box DNA-binding sequences (CAnnTG), when tested in the context of their native genomic flanking sequences, are bound differently by Cbf1 and Tye7. computational models of the PBM data indicate that DNA sequence features located in the genomic sequences outside the E-box contribute to DNA-binding specificityin vitro. Our analyses suggest that these flanking regions affect DNA-binding specificity indirectly by influencing the three-dimensional structure of the E-box binding sites. Finally, we show that these subtle differences in intrinsic sequence preferences of Cbf1 and Tye7in vitrohelp to explain their differential DNA-binding preferencesin vivo. Our results provide further evidence that the local shape of DNA-binding sites may be an important feature in distinguishing the DNA-binding preferences among paralogous TFs and thus may play a widespread role in determining how transcriptional regulatory specificity within TF families is achieved.

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A large-scale binding and functional map of human RNA-binding proteins (vol 583, pg 711, 2020)

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NATURE 2021年第7842期589卷 E5-E5页

作者： Van Nostrand, Eric L. Freese, Peter Pratt, Gabriel A. Wang, Xiaofeng Wei, Xintao Xiao, Rui Blue, Steven M. Chen, Jia-Yu Cody, Neal A. L. Dominguez, Daniel Olson, Sara Sundararaman, Balaji Zhan, Lijun Bazile, Cassandra Bouvrette, Louis Philip Benoit Bergalet, Julie Duff, Michael O. Garcia, Keri E. Gelboin-Burkhart, Chelsea Hochman, Myles Lambert, Nicole J. Li, Hairi McGurk, Michael P. Nguyen, Thai B. Palden, Tsultrim Rabano, Ines Sathe, Shashank Stanton, Rebecca Su, Amanda Wang, Ruth Yee, Brian A. Zhou, Bing Louie, Ashley L. Aigner, Stefan Fu, Xiang-Dong Lecuyer, Eric Burge, Christopher B. Graveley, Brenton R. Yeo, Gene W. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Medical Research Institute Wuhan University Wuhan China Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Institut de Recherches Cliniques de Montréal (IRCM) Montreal Quebec Canada Department of Biochemistry and Molecular Medicine Université de Montréal Montreal Quebec Canada Division of Experimental Medicine McGill University Montreal Quebec Canada Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA

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Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad

arXiv

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arXiv 2024年

作者： Warrell, Jonathan Alesiani, Francesco Smith, Cameron Mösch, Anja Min, Martin Renqiang NEC Laboratories America Machine Learning Department PrincetonNJ08540 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States NEC Laboratories Europe Kurfuerstenanlage 36 HeidelbergD-69115 Germany Molecular Pathology Unit Center for Cancer Research Massachusetts General Hospital Research Institute Department of Pathology Harvard Medical School BostonMA United States Broad Institute of MIT and Harvard CambridgeMA United States

Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price's Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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