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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Leone, Samuel Venkat, Aarthi Huguet, Guillaume Tong, Alexander Wolf, Guy Krishnaswamy, Smita Applied Math Program New HavenCT United States Yale University Computational Biology & Bioinformatics Program New HavenCT United States Université de Montréal Mila - Quebec Ai Institute Dept. of Math. & Stat Montreal Canada Université de Montréal Mila-Quebec Ai Institute Dept. of Comp. Sci. & Oper. Res. Montreal Canada Yale University Department of Computer Science Department of Genetics New HavenCT United States

ISBN: (纸本)9798350328851

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes the difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization. © 2023 IEEE.

关键词： Genes

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Bayesian multinomial logistic normal models through marginally latent matrix-T processes

arXiv

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arXiv 2019年

作者： Silverman, Justin D. Roche, Kimberly Holmes, Zachary C. David, Lawrence A. Mukherjee, Sayan Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Medical Scientist Training Program Duke University DurhamNC27708 United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University DurhamNC27708 United States Center for Genomic and Computational Biology Duke University DurhamNC27708 United States Department of Molecular Genetics and Microbiology Duke University DurhamNC27708 United States

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to handling small data sets due to the non-conjugacy of the multinomial and logistic-normal distributions. We introduce MLN models which can be written as marginally latent matrix-t process (LTP) models. Marginally LTP models describe a flexible class of generalized linear regression, non-linear regression, and time series models. We develop inference schemes for Marginally LTP models and, through application to MLN models, demonstrate that our inference schemes are both highly accurate and often 4-5 orders of magnitude faster than MCMC. Copyright © 2019, The Authors. All rights reserved.

关键词： Multivariant analysis

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Network Analysis as a Grand Unifier in Biomedical Data science

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Biomedical Data science 1000年第1期1卷 153-180页

作者： Patrick McGillivray Declan Clarke William Meyerson Jing Zhang Donghoon Lee Mengting Gu Sushant Kumar Holly Zhou Mark Gerstein 1Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA email: mark@*** 2Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA 3Department of Computer Science Yale University New Haven Connecticut 06520 USA

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

关键词： network analysis molecular interaction systems biology cross-disciplinary research network medicine

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InCoB celebrates its tenth anniversary as first joint conference with ISCB-Asia

InCoB celebrates its tenth anniversary as first joint confer...

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10th International Conference on bioinformatics and 1st ISCB Asia Joint Conference 2011: computational biology, InCoB 2011/ISCB-Asia 2011

作者： Schönbach, Christian Tan, Tin Wee Kelso, Janet Rost, Burkhard Nathan, Sheila Ranganathan, Shoba Department of Bioscience and Bioinformatics Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka 820-8502 Japan Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Department of Evolutionary Genetics Max Planck Institute for Evolutionary Anthropology 04103 Leipzig Germany Department for Bioinformatics and Computational Biology Faculty for Informatics Technical University Munich 85748 Garching Germany Faculty of Science and Technology School of Biosciences and Biotechnology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor D.E. Malaysia Malaysia Genome Institute Jalan Bangi 43000 Kajang Selangor D. E. Malaysia Department of Chemistry and Biomolecular Sciences ARC Centre of Excellence in Bioinformatics Macquarie University Sydney NSW 2109 Australia

In 2009 the International Society for computational biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia (ISCBAsia) was awarded to Asia-Pacific bioinformatics Network (APBioNet) which has been running the International Conference on bioinformatics (InCoB) in the Asia-Pacific region since 2002. InCoB/ISCB-Asia 2011 is held from November 30 to December 2, 2011 in Kuala Lumpur, Malaysia. Of 104 manuscripts submitted to BMC Genomics and BMC bioinformatics conference supplements, 49 (47.1%) were accepted. The strong showing of Asia among submissions (82.7%) and acceptances (81.6%) signals the success of this tenth InCoB anniversary meeting, and bodes well for the future of ISCB-Asia. © 2011 licensee BioMed Central Ltd.

关键词： bioinformatics

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Multi-task learning via conic programming 07

Multi-task learning via conic programming

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Proceedings of the 21st International Conference on Neural Information Processing Systems

作者： Tsuyoshi Kato Hisashi Kashima Masashi Sugiyama Kiyoshi Asai Graduate School of Frontier Sciences The University of Tokyo and Institute for Bioinformatics Research and Development (BIRD) Japan Science and Technology Agency (JST) Tokyo Research Laboratory IBM Research Department of Computer Science Tokyo Institute of Technology Graduate School of Frontier Sciences The University of Tokyo and AIST Computational Biology Research Center

ISBN: (纸本)9781605603520

When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and has been studied extensively. Existing approaches to MTL often treat all the tasks as uniformly related to each other and the relatedness of the tasks is controlled globally. For this reason, the existing methods can lead to undesired solutions when some tasks are not highly related to each other, and some pairs of related tasks can have significantly different solutions. In this paper, we propose a novel MTL algorithm that can overcome these problems. Our method makes use of a task network, which describes the relation structure among tasks. This allows us to deal with intricate relation structures in a systematic way. Furthermore, we control the relatedness of the tasks locally, so all pairs of related tasks are guaranteed to have similar solutions. We apply the above idea to support vector machines (SVMs) and show that the optimization problem can be cast as a second order cone program, which is convex and can be solved efficiently. The usefulness of our approach is demonstrated through simulations with protein super-family classification and ordinal regression problems.

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Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts

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Briefings in bioinformatics 2023年第2期24卷 bbac633-bbac633页

作者： Tu, Yueh-Hua Juan, Hsueh-Fen Huang, Hsuan-Cheng The Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taiwan Department of Life Science Center for Computational and Systems Biology National Taiwan University Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level. © The Author(s) 2023.

关键词： Gaussian mixture model gene regulatory network single-cell RNA sequencing trajectory analysis

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Connecting polymer collapse and the onset of jamming

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Physical Review E 2024年第3期109卷 034406-034406页

作者： Alex T. Grigas Aliza Fisher Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Previous studies have shown that the interiors of proteins are densely packed, reaching packing fractions that are as large as those found for static packings of individual amino-acid-shaped particles. How can the interiors of proteins take on such high packing fractions given that amino acids are connected by peptide bonds and many amino acids are hydrophobic with attractive interactions? We investigate this question by comparing the structural and mechanical properties of collapsed attractive disk-shaped bead-spring polymers to those of three reference systems: static packings of repulsive disks, of attractive disks, and of repulsive disk-shaped bead-spring polymers. We show that the attractive systems quenched to temperatures below the glass transition T≪Tg and static packings of both repulsive disks and bead-spring polymers possess similar interior packing fractions. Previous studies have shown that static packings of repulsive disks are isostatic at jamming onset, i.e., the number of interparticle contacts Nc matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymer packings are hypostatic at jamming onset (i.e., with fewer contacts than degrees of freedom) but are effectively isostatic when including stabilizing quartic modes, which give rise to quartic scaling of the potential energy with displacements along these modes. While attractive disk and polymer packings are often considered hyperstatic with excess contacts over the isostatic number, we identify a definition for interparticle contacts for which they can also be considered as effectively isostatic. As a result, we show that the mechanical properties (e.g., scaling of the potential energy with excess contact number and low-frequency contribution to the density of vibrational modes) of weakly attractive disk and polymer packings are similar to those of isostatic repulsive disk and polymer packings. Our results demonstrate that static

关键词： Jamming Polymer folding Protein folding Single polymer chains

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Computing H/D-Exchange rates of single residues from data of proteolytic fragments

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BMC bioinformatics 2010年第1期11卷 1-12页

作者： Althaus, Ernst Canzar, Stefan Ehrler, Carsten Emmett, Mark R. Karrenbauer, Andreas Marshall, Alan G. Meyer-Bäse, Anke Tipton, Jeremiah D. Zhang, Hui-Min Institut für Informatik Fachbereich 08 Staudingerweg 9 55099 Mainz Germany Algorithmic Computational Biology Centrum Wiskunde and Informatica Amsterdam Netherlands Center for Bioinformatics Computer Science Department Saarland University 66041 Saarbrücken Germany Ion Cyclotron Resonance Program National High Magnetic Field Laboratory Florida State University 1800 East Paul Dirac Drive Tallahassee FL 32310-4005 United States Department of Chemistry and Biochemistry Florida State University 95 Chieftain Way Tallahassee FL 32306 United States Institute of Mathematics EPFL Station 8 1015 Lausanne Switzerland Department of Scientific Computing Florida State University Tallahassee FL 32306-4120 United States

Background: Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein complex. In sHDX experiments mutant proteins are compared to wild-type proteins or a ligand is added to the protein and compared to the wild-type protein (or mutant). The number of deuteriums incorporated into the polypeptides generated from the protease digest of the protein is related to the solvent accessibility of amide protons within the original protein ***: In this work, sHDX data was collected on a 14.5 T FT-ICR MS. An algorithm was developed based on combinatorial optimization that predicts deuterium exchange with high spatial resolution based on the sHDX data of overlapping proteolytic fragments. Often the algorithm assigns deuterium exchange with single residue ***: With our new method it is possible to automatically determine deuterium exchange with higher spatial resolution than the level of digested fragments. © 2010 Althaus et al;licensee BioMed Central Ltd.

关键词： Proteins

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Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference

Towards big data science in the decade ahead from ten years ...

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10th International Conference on bioinformatics and 1st ISCB Asia Joint Conference 2011: bioinformatics, InCoB 2011/ISCB-Asia 2011

作者： Ranganathan, Shoba Schönbach, Christian Kelso, Janet Rost, Burkhard Nathan, Sheila Tan, Tin Wee Department of Chemistry and Biomolecular Sciences ARC Centre of Excellence in Bioinformatics Macquarie University Sydney NSW 2109 Australia Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Department of Bioscience and Bioinformatics Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka 820-8502 Japan Department of Evolutionary Genetics Max Planck Institute for Evolutionary Anthropology 04103 Leipzig Germany Department for Bioinformatics and Computational Biology Faculty for Informatics Technical University Munich 85748 Garching Germany School of Biosciences and Biotechnology Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor D.E. Malaysia Malaysia Genome Institute Jalan Bangi 43000 Kajang Selangor D.E. Malaysia

The 2011 International Conference on bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for computational biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB's goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/ proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design. © 2011 Ranganathan et al;licensee BioMed Central Ltd.

关键词： bioinformatics

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FaNDOM: Fast Nested Distance-Based Seeding of Optical Maps

SSRN

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SSRN 2021年

作者： Dehkordi, Siavash Raeisi Luebeck, Jens Bafna, Vineet Department of Computer Science & Engineering University of California San Diego San diegoCA92093 United States Bioinformatics & Systems Biology Graduate Program University of California San Diego San diegoCA92093 United States

Optical mapping provides single-molecule readouts of the locations of fluorescently labeled sequence motifs on long fragments of DNA, resolved to nucleotide-level coordinates. With the advent of microfluidic technologies for manipulating large numbers of individual molecules, and advances in the imaging of fluorescent DNA tags, it is possible to inexpensively generate high coverage and long OM molecules (>150Kbp). In addition to its use in scaffolding for de novo assembly, optical map data can also be mapped to a reference for identifying genetic structural variants implicated in Mendelian diseases and cancer. To address the computational challenge, we introduce FaNDOM (Fast Nested Distance seeding of Optical Maps) - an optical map alignment tool which greatly reduces the search space of the alignment process. On 4 benchmark human data-sets, FaNDOM was significantly (4-14X) faster than competing tools while maintaining comparable sensitivity and specificity. We used FaNDOM to map variants in 3 cancer cell-lines and identified many structural variations, including deletion of tumor-suppressor genes, duplications, gene fusions and gene-disrupting rearrangements. FaNDOM is publicly available at (https://***/jluebeck/FaNDOM). © 2021, The Authors. All rights reserved.

关键词： Genes

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