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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ya-Wei Eileen Lin Yuval Kluger Ronen Talmon Viterbi Faculty of Electrical and Computer Engineering Technion Interdepartmental Program in Computational Biology and Bioinformatics Yale University Applied Math Program Yale University Department of Pathology School of Medicine Yale University

In this paper, we present Hyperbolic Diffusion Procrustes Analysis (HDPA), a new method for informative representation of hierarchical datasets based on hyperbolic geometry, diffusion geometry, and Procrustes analysis. Our method jointly embeds multiple datasets in a product manifold of hyperbolic spaces, where the data's hidden common hierarchical structure is provably recovered. In addition, our method generates an intrinsic embedding that accommodates the joint representation of multiple datasets with different features, acquired by different equipment, at different sites, or under different environmental conditions. Experimental results demonstrate the efficacy of HDPA on three biomedical datasets comprising heterogeneous gene expression and mass cytometry data.

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Association between response to brief trastuzumab exposure in cell lines and early stage HER2+ breast tumors 11

Association between response to brief trastuzumab exposure i...

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2011 ACM Conference on bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Sprecher, Emmett Sarkar, Sudipa Kleinstein, Steven H. Narayan, Murli Winer, Eric Tuck, David Lezon-Geyda, Kimberly Krop, Ian Harris, Lyndsay Department of Pathology Pathology Informatics United States Interdepartmental Program in Computational Biology and Bioinformatics United States Medical Oncology Yale Cancer Center United States School of Medicine Yale University New Haven CT United States Department of Medical Oncology Dana-Farber Cancer Institute Boston MA United States

ISBN: (纸本)9781450307963

Human epidermal growth factor receptor 2 amplified (HER2+) breast cancer is common and aggressive. Trastuzumab is a targeted therapy to the HER2 cell surface receptor and has greatly improved prognosis for HER2+ breast cancer patients, but trastuzumab-resistance is a significant problem. Few biomarkers of trastuzumab resistance of response have been found, and there has been tremendous difficulty identifying in vitro biomarkers that transfer to the in vivo HER2+ breast cancer setting. We identified gene expression signatures of trastuzumabresponse and trastuzumab-resistance in HER2+ breast cancer cell lines across 24-hour exposure to trastuzumab. A set of 180-genes changed significantly in a trastuzumab-sensitive HER2+ cell line (BT474) and 58 genes changed significantly across treatment in a trastuzumab-resistant HER2+ cell line (UACC812). To demonstrate that these signatures have clinical relevance, we applied them in vivo to gene expression profiles from early stage HER2+ breast tumors treated with a single dose of trastuzumab. We found that the BT474 trastuzumabresponsive signature was enriched among the changes in expression across treatment for responsive patients. The expression change for the BT474 signature genes was also able to partially cluster responsive and resistant breast tumors by outcome. A subset of the UACC812 trastuzumab-resistance signature was also enriched in the differential between responsive and resistant tumors prior to trastuzumab treatment. These results show for the first time that cell line-derived gene signatures can be useful in identifying whether breast cancer patients will ultimately benefit from trastuzumab treatment. This approach might one day be applied to enhance clinical treatment decisions. Copyright © 2011 ACM.

关键词： Gene expression

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A Study of Classification Techniques Based on Spike Protein Sequences of MERS-CoV 23

A Study of Classification Techniques Based on Spike Protein ...

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Proceedings of the 2023 10th International Conference on Biomedical and bioinformatics Engineering

作者： Hayeon Kim Myeongji Cho Hyeon S. Son Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea and Institute of Health and Environment Seoul National University Republic of Korea Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea Laboratory of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Republic of Korea Institute of Health and Environment Seoul National University Republic of Korea and Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Republic of Korea

ISBN: (纸本)9798400708343

MERS-CoV, which belongs to the beta-coronaviruses together with SARS-CoV-2, although it has received relatively less attention by the COVID-19 pandemic, there is a sufficient possibility of new MERS-CoV lineages and variants. Previous studies have discussed the possibility of frequent recombination of MERS-CoV. We thus present a highly accurate method for the phylogenetic analysis and classification of MERS-CoV including recombinant sequences. We collected the sequences of S protein from MERS-CoV and divided them into five phylogenetic groups, of which recombinant sequences were divided into seven types. Physicochemical properties of amino acids were then calculated from the S protein sequences, and the results were used for the random forest model, Naïve Bayes classification, and k-nearest neighbor method. We also constructed several feature subsets based on the ranked amino acid properties and applied them to the random forest model. In each dataset, the amino acid physicochemical properties were ranked differently. Using this information, classification of MERS-CoV based on machine learning algorithms showed that the random forest model had the best accuracy and area under the curve compared with the k-nearest neighbor and Naïve Bayes classification methods. Several feature subsets were constructed using the correlation feature selection algorithm and applied to the random forest model. Overall, the performance of the classifier was improved compared to that when using all features. Coronaviruses including MERS-CoV continue to evolve into new forms through recombination or mutation. We thus present a method to increase the accuracy of their classification using additional information of the viral protein sequence, and confirm that a subunit consisting of optimal prominent features can improve the performance of the classifier by removing the unnecessary characteristic information.

关键词： MERS-CoV classification machine learning

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Crystallization of asymmetric patchy models for globular proteins in solution

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Physical Review E 2013年第1期88卷 012721-012721页

作者： Diana Fusco Patrick Charbonneau Program in Computational Biology and Bioinformatics Duke University Durham North Carolina 27708 USA Department of Chemistry Duke University Durham North Carolina 27708 USA Department of Physics Duke University Durham North Carolina 27708 USA

Asymmetric patchy particle models have recently been shown to describe the crystallization of small globular proteins with near-quantitative accuracy. Here, we investigate how asymmetry in patch geometry and bond energy generally impacts the phase diagram and nucleation dynamics of this family of soft matter models. We find the role of the geometry asymmetry to be weak, but the energy asymmetry to markedly interfere with the crystallization thermodynamics and kinetics. These results provide a rationale for the success and occasional failure of the proposal of George and Wilson for protein crystallization conditions as well as physical guidance for developing more effective protein crystallization strategies.

关键词： Crystallization asymmetric globular Crystallization soft matter Solutions globular protein globular shape Bond energy ASYMMETRIC Phase diagrams

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Ribosomes guide pachytene piRNA formation on long intergenic piRNA precursors (vol 22, pg 200, 2020)

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NATURE CELL biology 2020年第3期22卷 353-353页

作者： Sun, Yu H. Zhu, Jiang Xie, Li Huitong Li, Ziwei Meduri, Rajyalakshmi Zhu, Xiaopeng Song, Chi Chen, Chen Ricci, Emiliano P. Weng, Zhiping Li, Xin Zhiguo Center for RNA Biology: From Genome to Therapeutics Department of Biochemistry and Biophysics University of Rochester Medical Center Rochester NY USA Computational Biology Department Carnegie Mellon University Pittsburgh PA USA College of Public Health Division of Biostatistics The Ohio State University Columbus OH USA Department of Animal Science Michigan State University East Lansing MI USA Université de Lyon ENSL UCBL INSERM CNRS LBMC Lyon France Program in Bioinformatics and Integrative Biology University of Massachusetts Medical School Worcester MA USA Department of Urology University of Rochester Medical Center Rochester NY USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： RNA Spermatogenesis

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Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex

Cell Genomics

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Cell Genomics 2025年 100879页

作者： Nelson J. Johansen Niklas Kempynck Nathan R. Zemke Saroja Somasundaram Seppe De Winter Marcus Hooper Deepanjali Dwivedi Ruchi Lohia Fabien Wehbe Bocheng Li Darina Abaffyová Ethan J. Armand Julie De Man Eren Can Ekşi Nikolai Hecker Gert Hulselmans Vasilis Konstantakos David Mauduit John K. Mich Gabriele Partel Trygve E. Bakken Allen Institute for Brain Science Seattle WA 98109 USA VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics Leuven Belgium Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA 92093 USA Physiology Department and Donnelly Centre for Cellular and Biomolecular Research University of Toronto Toronto ON Canada Maisonneuve-Rosemont Hospital Research Centre University of Montreal Montreal QC Canada School of Life Sciences Westlake University Hangzhou Zhejiang China Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA 92093 USA

Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the “Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics” to evaluate machine learning and feature-based methods for nominating enhancer sequences targeting mouse cortical cell types. Methods were assessed using in vivo data from hundreds of adeno-associated virus (AAV)-packaged, retro-orbitally delivered enhancers. Open chromatin was the strongest predictor of functional enhancers, while sequence models improved prediction of non-functional enhancers and identified cell-type-specific transcription factor codes to inform in silico enhancer design. This challenge establishes a benchmark for enhancer prioritization and highlights computational and molecular features critical for identifying functional cortical enhancers, advancing efforts to map and manipulate gene regulation in the mammalian cortex.

关键词： enhancer-AAV DNA sequence model TF codes single-cell multiomics cortex prediction benchmark cross-species ATAC-seq

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Heterogeneous photocatalytic degradation of diuron on zinc oxide: Influence of surface-dependent adsorption on kinetics,degradation pathway,and toxicity of intermediates

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Journal of Environmental Sciences 2019年第10期31卷 97-111页

作者： Sutaporn Meephon Thanyada Rungrotmongkol Somchintana Puttamat Supareak Praserthdam Varong Pavarajarn Center of Excellence in Particle Technology Department of Chemical Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand Biocatalyst and Environmental Biotechnology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn UniversityBangkok 10330 Thailand Bioinformatics and Computational Biology Program Graduated School Chulalongkorn University Bangkok 10330 Thailand Department of Chemistry Faculty of Science Thammasat University Pathumthani 12120 Thailand Center of Excellence on Catalysis and Catalytic Reaction Engineering Department of Chemical Engineering Faculty of EngineeringChulalongkorn University Bangkok 10330 Thailand Research Program in Hazardous Substance Management in Agricultural Industry Center of Excellence on Hazardous Substance Management(HSM) Bangkok 10330 Thailand

Heterogeneous photocatalytic reaction has been generally applied for degradation of toxic *** of a compound using the same kind of catalyst that was synthesized differently are commonly found in ***,the reported degradation intermediates are normally *** issue is especially important for the degradation of toxic compounds because intermediates may be more toxic than their parent compounds and understanding the reason is necessary if appropriate catalysts are to be *** work systematically compares the photocatalytic degradation of diuron,a toxic recalcitrant herbicide,on two forms of zinc oxide(ZnO),i.e.,conventional particles with zinc-and oxygen-terminated polar surfaces as the dominating planes,and nanorods with mixed-terminated nonpolar *** and theoretical results indicate that both the rate of reaction and the degradation pathway depend on the adsorption configuration of diuron onto the *** molecules adsorb in different alignments on the two surfaces,contributing to the formation of different degradation *** the aliphatic and aromatic sides of diuron adsorb on the polar surfaces simultaneously,leading to an attack by hydroxyl radicals from both *** the other hand,on the mixed-terminated surface,only the aliphatic part adsorbs and is *** exposed surface is therefore the key factor controlling the degradation *** diuron degradation on ZnO,a catalyst confined tomixed-terminated surfaces,i.e.,ZnO nanorods,is more desirable,as it avoids the formation of intermediates with potent phytotoxicity and cytogenotoxicity.

关键词： Diuron Photocatalysis Adsorption Degradation pathway Mechanism Toxicity

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Data-driven modeling and prediction of microglial cell dynamics in the ischemic penumbra

arXiv

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arXiv 2024年

作者： Amato, Sara Arnold, Andrea Bioinformatics & Computational Biology Program Worcester Polytechnic Institute WorcesterMA United States Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States

Neuroinflammation immediately follows the onset of ischemic stroke. During this process, microglial cells are activated in and recruited to the tissue surrounding the irreversibly injured infarct core, referred to as the penumbra. Microglial cells can be activated into two distinct phenotypes;however, the dynamics between the detrimental M1 phenotype and beneficial M2 phenotype are not fully understood. Using phenotype-specific cell count data obtained from experimental studies on middle cerebral artery occlusion-induced stroke in mice, we employ sparsity-promoting system identification techniques combined with Bayesian statistical methods for uncertainty quantification to generate continuous and discrete-time predictive models of the M1 and M2 microglial cell dynamics. The resulting data-driven models include constant and linear terms but do not include nonlinear interactions between the cells. Results emphasize an initial M2 dominance followed by a takeover of M1 cells, capture potential long-term dynamics of microglial cells, and suggest a persistent inflammatory response. Copyright © 2024, The Authors. All rights reserved.

关键词： Mammals

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Identifying interaction sites in "Recalcitrant" proteins: Predicted protein and RNA binding sites in REV proteins of HIV-1 and EIAV agree with experimental data

Identifying interaction sites in "Recalcitrant" proteins: Pr...

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11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Terribllini, Michael Lee, Jae-Hyung Yan, Changhui Jernigan, Robert L. Carpenter, Susan Honavar, Vasant Dobbs, Drena Bioinformatics and Computational Biology Graduate Program LH. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50010 United States Department of Computer Science Iowa State University Ames IA 50010 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50010 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50010 United States Artificial Intelligence Research Laboratory Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA 50010 United States Department of Veterinary Microbiology and Pathology Washington State University Pullman WA. 99164 United States

ISBN: (纸本)9812564632

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation or gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the proiein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-I and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.

关键词： Binding sites

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Scalable algorithms for learning high-dimensional linear mixed models

arXiv

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arXiv 2018年

作者： Tan, Zilong Roche, Kimberly Zhou, Xiang Mukherjee, Sayan Department of Computer Science Duke University Program in Computational Biology & Bioinformatics Duke University Department of Biostatistics Center for Statistical Genetics University of Michigan Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University

Linear mixed models (LMMs) are used extensively to model dependecies of observations in linear regression and are used extensively in many application areas. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends at least linearly on the dimension p of the covariates, and often use heuristics that do not offer theoretical guarantees. We present scalable algorithms for learning high-dimensional LMMs with sublinear computational complexity dependence on p. Key to our approach are novel dual estimators which use only kernel functions of the data, and fast computational techniques based on the subsampled randomized Hadamard transform. We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation. Finally, we complement the theory with experiments on large synthetic and real data. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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