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BPS2025 - Characterizing oligomeric amyloid-β42 and POPC interactions through molecular dynamics: A computational approach for understanding Alzheimer’s disease

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Biophysical Journal 2025年第3期124卷 70a-70a页

作者： Emma M. Cleveland Kelsie M. King Anne M. Brown Systems Biology Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech University Blacksburg VA USA

A probabilistic model for pathway-guided gene set selection

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A probabilistic model for pathway-guided gene set selection

2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Kim, Inyoung Lee, Sangseon Kim, Youngkuk Namkoong, Hugh Kim, Sun Artificial Intelligence Institute Seoul National University Seoul Korea Republic of Institute of Computer Technology Seoul National University Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of Institute of Engineering Research Seoul National University Seoul Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665401265

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation. © 2021 IEEE.

关键词： Gene expression

SRAMM: Short read alignment mapping metrics

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arXiv 2021年

作者： Chon, Alvin Huang, Xiaoqiu Bioinformatics and Computational Biology Graduate Program Iowa State University AmesIA50010 United States Department of Computer Science Iowa State University AmesIA50010 United States

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://***/achon/sramm. © 2021, CC BY.

关键词： Mapping

Flow and clogging of capillary droplets

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arXiv 2024年

作者： Cheng, Yuxuan Lonial, Benjamin F. Sista, Shivnag Meer, David J. Hofert, Anisa Weeks, Eric R. Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Capillary droplets form due to surface tension when two immiscible fluids are mixed. We describe the motion of gravity-driven capillary droplets flowing through narrow constrictions and obstacle arrays in both simulations and experiments. Our new capillary deformable particle model recapitulates the shape and velocity of single oil droplets in water as they pass through narrow constrictions in microfluidic chambers. Using this experimentally validated model, we simulate the flow and clogging of single capillary droplets in narrow channels and obstacle arrays and find several important results. First, the capillary droplet speed profile is nonmonotonic as the droplet exits the narrow orifice, and we can tune the droplet properties so that the speed overshoots the terminal speed far from the constriction. Second, in obstacle arrays, we find that extremely deformable droplets can wrap around obstacles, which leads to decreased average droplet speed in the continuous flow regime and increased probability for clogging in the regime where permanent clogs form. Third, the wrapping mechanism causes the clogging probability in obstacle arrays to become nonmonotonic with surface tension Γ. At large Γ, the droplets are nearly rigid and the clogging probability is large since the droplets can not squeeze through the gaps between obstacles. With decreasing Γ, the clogging probability decreases as the droplets become more deformable. However, in the small-Γ limit, the clogging probability increases since the droplets are extremely deformable and wrap around the obstacles. The results from these studies are important for developing a predictive understanding of capillary droplet flows through complex and confined geometries. © 2024, CC BY.

关键词： Drop formation

Analysis of protein determinants of genotype-specific properties of group a rotaviruses using machine learning

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Computers in Biology and Medicine 2025年 191卷 110143-110143页

作者： Cho, Myeongji Been, Nara Son, Hyeon S. Laboratory of Computational Virology & Viroinformatics of Public Health Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Public Health AI Lab of Public Health Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Institute of Health and Environment Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

Group A rotaviruses (RVAs) are the leading cause of viral diarrhoea across various host species, including mammals and birds. The VP7 and VP4 proteins of these viruses play critical roles in determining genotype specificity, influencing viral infectivity and host adaptation. This study employed machine-learning techniques to classify RVA genotypes based on the molecular and physicochemical properties of these proteins. A dataset of 94 VP7 and 68 VP4 protein sequences was collected from various host species. Seven machine-learning algorithms—Naïve Bayes (NB), logistic regression (LR), decision tree (DT), random forest (RF), k-nearest neighbour (kNN), support vector machine (SVM), and artificial neural network (ANN)—were used for genotype classification. Feature subsets were configured using ranking-based attribute selection, and classification performance was evaluated using accuracy (ACC), precision, recall, Matthews’ correlation coefficient (MCC), and the area under the curve (AUC). kNN demonstrated the highest classification accuracy for both VP7 (ACC = 97.87 %) and VP4 (ACC = 100 %), outperforming NB, LR, DT, RF, SVM, and ANN. For VP7 sequences, key properties influencing genotype classification included hydrophobicity, normalised van der Waals volume, and leucine composition. For VP4, polarity, normalised van der Waals volume, and polarizability were the most significant factors. In summary, the genotype-specific molecular features of VP7 and VP4 proteins served as reliable markers for RVA classification. Our findings highlight the potential of machine-learning approaches to predict RVA genotypes based on the physicochemical properties of amino acids, providing valuable insights into the molecular mechanisms that drive viral evolution, host specificity, and immune evasion. © 2025 Elsevier Ltd

关键词： Adversarial machine learning

Predicting regional somatic mutation rates using DNA motifs

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PLoS Computational Biology 2023年第10 October期19卷 e1011536-e1011536页

作者： Liu, Cong Wang, Zengmiao Wang, Jun Liu, Chengyu Wang, Mengchi Ngo, Vu Wang, Wei Department of Chemistry and Biochemistry University of California San Diego La Jolla CA United States State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health Faculty of Geographical Science Beijing Normal University Beijing China Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA United States

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature. © 2023 Liu et al.

关键词： Neural network models

SPGP: Structure Prototype Guided Graph Pooling

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arXiv 2022年

作者： Lee, Sangseon Lee, Dohoon Piao, Yinhua Kim, Sun Institute of Computer Technology Seoul National University Korea Republic of BK21 FOUR Intelligence Computing Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Korea Republic of

While graph neural networks (GNNs) have been successful for node classification tasks and link prediction tasks in graph, learning graph-level representations still remains a challenge. For the graph-level representation, it is important to learn both representation of neighboring nodes, i.e., aggregation, and graph structural information. A number of graph pooling methods have been developed for this goal. However, most of the existing pooling methods utilize k-hop neighborhood without considering explicit structural information in a graph. In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation. SPGP formulates graph structures as learnable prototype vectors and computes the affinity between nodes and prototype vectors. This leads to a novel node scoring scheme that prioritizes informative nodes while encapsulating the useful structures of the graph. Our experimental results show that SPGP outperforms state-of-the-art graph pooling methods on graph classification benchmark datasets in both accuracy and scalability. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

Single cell transcriptomic profiling of neurodegeneration mediated by tau propagation in a novel 3D neuron-astrocyte coculture model

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Research Square

Research Square 2021年

作者： Rickner, Hannah Jiang, Lulu Hong, Rui O'Neill, Nicholas K. Wolozin, Benjamin Cheng, Christine Department of Biology Boston University BostonMA02215 United States Department of Pharmacology and Experimental Therapeutics Boston University School of Medicine BostonMA02118 United States Program in Bioinformatics Boston University BostonMA02215 United States Department of Biology & Program in Bioinformatics Boston University BostonMA02215 United States Informatics Group J. Craig Venter Institute La Jolla CA92037 United States

Research into neurodegeneration has been hampered by lack of systems that accurately recapitulate neurodegenerative processes1. Propagation of tau through a ‘prion-like’ process has emerged as an important aspect of neurodegenerative diseases including Alzheimer’s disease2. However, molecular mechanism of tau propagation is still largely unknown and a human 3D cellular model is still lacking. Here, we report development of the AstAD system, which uses human iPSCs to create neuron-astrocyte spheroids that incorporates propagation of toxic tau oligomers3,4. Single cell transcriptomic profiling reveals roles for ribosomes, TNF mediated neuroinflammation and heat shock proteins (HSP) as major elements of the disease stress response. Treatment with the HSP90 inhibitor PU-H71, which is selective for the dysfunctional HSP epichaperome, demonstrates reduction of pathology and neurodegeneration in the AstAD system5. © 2021, CC BY.

关键词： Oligomers

Graph Fourier MMD for Signals on Graphs

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arXiv 2023年

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

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 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. Copyright © 2023, The Authors. All rights reserved.

关键词： Genes