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作者： Burger, Virginia Chennubhotla, Chakra Joint CMU-Pitt Ph.D. Program in Computational Biology University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh PA United States

Cryo-electron microscopy (cryo-EM) experiments yield low-resolution (3-30 Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and subunits. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semisupervised computational methods, whereas validation of resulting segmentations has remained an open problem in this field. We introduce a network-based hierarchical segmentation (Nhs) method, that provides a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and weighted edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant subregions at multiple scales. © 2012 Wiley Periodicals, Inc.

关键词： Diffusion

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Towards cross-modal causal structure and representation learning 2

Towards cross-modal causal structure and representation lear...

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2nd Machine Learning for Health Symposium, ML4H 2022

作者： Mao, Haiyi Liu, Hongfu Xiaotian Dou, Jason Benos, Panayiotis V. Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh United States Michtom School of Computer Science Brandeis University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Epidemiology University of Florida United States

Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an IDH-mutation cause differences in patients' MRI images? Conventional causal discovery algorithms, although well developed to uncover the cause-effect relationships on structured data, cannot elucidate causal relations between unstructured images and structured scalar variables due to the complexity of the former. In this paper, we consider causal discovery between images and structured (scalar) variables. Specifically, we derive low dimensional image representations to analyze with structured variables. We propose a two-module amortized variational algorithm named Cross-Modal Variational Causal representation and structure Learning (CMCL). CMCL jointly learns identifiable representations given a set of independent structured variables and causal relations via formulating latent representations and structured variables into a direct acyclic graph. Moreover, we further enforce counterfactual invariance/variance onto representations. We demonstrate that CMCL outperforms other related methods on synthetic datasets and validate causal relations on semi-synthetic datasets by visualization. © 2022 P.N. Argaw, E. Healey & I.S. Kohane.

关键词： Coronavirus

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Spectral clustering strategies for heterogeneous disease expression data 18

Spectral clustering strategies for heterogeneous disease exp...

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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

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Simultaneous identification for synergistic transcription factors and their transcription factor binding sites

Simultaneous identification for synergistic transcription fa...

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5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

作者： Tsai, Zing Tsung-Yeh Huang, Grace Tzu-Wei Tsai, Huai-Kuang Institute of Information Science Academia Sinica Taipei 115 Taiwan Bioinformatics Program Taiwan International Graduate Program Taiwan Institute of Biomedical Informatics National Yang-Ming University Taiwan Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh Carnegie Mellon University Pittsburgh PA United States Research Center for Information Technology Innovation Taipei 115 Taiwan Computer Science and Engineering National Taiwan Ocean University Keelung 202 Taiwan

ISBN: (纸本)9780769543734

Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While traditional approaches can be used to discover binding motifs of a group of co-regulated genes, they often fail to accurately assign motifs to the corresponding TFs. Here, we consider two TFs together to infer their TFBSs and their synergistic relationship simultaneously. The basic idea is that if two TFs interact, their TFBSs, if distinct, would be conserved across species and coincided in the promoter regions of the genes they co-regulated. Applying our method to Saccharomyces cerevisiae chromatin immunoprecipitation data, we predicted 110 TF pairs with statistically significant motif assignments. A majority of these TF pairs have literature support to be synergistic, and the designated motifs to TFs match well with their known consensus. We further examined the synergism of predicted TF pairs in seven experimental conditions using ANOVA, and identified significant interactions. © 2011 IEEE.

关键词： Yeast

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Categorical, low-dimensional decomposition of human odor space with non-negative matrix factorization

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Flavour 2014年第1期3卷 1-2页

作者： JB Castro SP Quinn A Ramanathan CS Chennubhotla Department of Psychology Bates College Lewiston USA Joint CMU-Pitt Ph.D. Program in Computational Biology University of Pittsburgh Pittsburgh USA Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge USA Department of Computational and Systems Biology University of Pittsburgh Pittsburgh USA

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Coalescent-based method for learning parameters of admixture events from large-scale genetic variation data 12

Coalescent-based method for learning parameters of admixture...

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2012 ACM Conference on Bioinformatics, computational biology and Biomedicine, BCB 2012

作者： Tsai, Ming-Chi Blelloch, Guy Ravi, R. Schwartz, Russell Joint CMU-Pitt Ph.D. Program in Computational Biology Carnegie Mellon University Pittsburgh PA United States Department of Computer Science Carnegie Mellon University Pittsburgh PA United States Tepper School of Business Carnegie Mellon University Pittsburgh PA United States Department of Biological Science Carnegie Mellon University Pittsburgh PA United States

ISBN: (纸本)9781450316705

Detecting and quantifying the timing and the genetic contributions of parental populations to a hybrid population is an important but challenging problem in reconstructing evolutionary histories from genetic variation data. With the advent of high throughput genotyping technologies, new methods suitable for large-scale data are especially needed. Furthermore, existing methods typically assume the assignment of individuals into subpopulations is known, when that itself is a difficult problem often unresolved for real data. Here we propose a novel method that combines prior work for inferring non-reticulate population structures with an MCMC scheme for sampling over admixture scenarios to both identify population assignments and learn divergence times and admixture proportions for those populations using genomescale admixed genetic variation data. We validated our method using coalescent simulations and a collection of real bovine and human variation data. On simulated sequences, our methods show better accuracy and faster runtime than leading competitive methods in estimating admixture fractions and divergence times. Analysis on the real data further shows our methods to be effective at matching our best current knowledge about the relevant populations. Copyright © 2012 ACM.

关键词： Genes

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Simultaneous Identification for Synergistic Transcription Factors and their Transcription Factor Binding Sites

Simultaneous Identification for Synergistic Transcription Fa...

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International Conference on Complex, Intelligent and Software Intensive Systems, CISIS

作者： Zing Tsung-Yeh Tsai Grace Tzu-Wei Huang Huai-Kuang Tsai Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taiwan Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh Carnegie Mellon University Pittsburgh USA Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan

关键词： Bioinformatics Analysis of variance DNA Databases Gene expression Proteins Stress

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Cell phenotypic transition proceeds through concerted reorganization of gene regulatory network

arXiv

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arXiv 2021年

作者： Wang, Weikang Poe, Dante Ni, Ke Xing, Jianhua Department of Computational and Systems Biology University of Pittsburgh PittsburghPA15232 United States Joint CMU-Pitt Ph.D. Program in Computational Biology University of Pittsburgh PittsburghPA United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15232 United States UPMC-Hillman Cancer Center University of Pittsburgh PittsburghPA United States

Phenotype transition takes place in many biological processes such as differentiation, and understanding how a cell reprograms its global gene expression profile is a problem of rate theories. A cell phenotype transition accompanies with switching of expression rates of clusters of genes, analogous to domain flipping in an Ising system. Here through analyzing single cell RNA sequencing data in the framework of transition path theory, we set to study how such a genome-wide expression program switching proceeds in three different cell transition processes. For each process after reconstructing a Markov transition model in the cell state space, we formed an ensemble of shortest paths connecting the initial and final cell states, reconstructed a reaction coordinate describing the transition progression, and inferred the gene regulation network (GRN) along the reaction coordinate. In all three processes we observed common pattern that the frustration of gene regulatory network (GRN), defined as overall confliction between the regulation received by genes and their expression states, first increases then decreases when approaching a new phenotype. The results support a mechanism of concerted silencing of genes that are active in the initial phenotype and activation of genes that are active in the final phenotype. © 2021, CC BY.

关键词： Ising model

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Publisher Correction: Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment

Nature cancer

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Nature cancer 2023年第4期4卷 582页

作者： Sayali Onkar Jian Cui Jian Zou Carly Cardello Anthony R Cillo Mostofa Rafid Uddin April Sagan Marion Joy Hatice U Osmanbeyoglu Katherine L Pogue-Geile Priscilla F McAuliffe Peter C Lucas George C Tseng Adrian V Lee Tullia C Bruno Steffi Oesterreich Dario A A Vignali Department of Immunology University of Pittsburgh School of Medicine Pittsburgh PA USA. Tumor Microenvironment Center UPMC Hillman Cancer Center Pittsburgh PA USA. Graduate Program in Microbiology and Immunology University of Pittsburgh School of Medicine Pittsburgh PA USA. Department of Biostatistics University of Pittsburgh Pittsburgh PA USA. Computational Biology Department Carnegie Mellon University Pittsburgh PA USA. Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology Pittsburgh PA USA. Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA USA. UPMC Hillman Cancer Center Pittsburgh PA USA. NSABP Foundation Pittsburgh PA USA. Department of Bioengineering University of Pittsburgh Pittsburgh PA USA. Section of Breast Surgery Division of Surgical Oncology Department of Surgery University of Pittsburgh College of Medicine Magee Women's Hospital of UPMC Pittsburgh PA USA. Women's Cancer Research Center UPMC Hillman Cancer Center Magee-Womens Research Institute University of Pittsburgh Pittsburgh PA USA. Department of Pathology University of Pittsburgh Pittsburgh PA USA. Cancer Biology Program UPMC Hillman Cancer Center Pittsburgh PA USA. Department of Pharmacology and Chemical Biology University of Pittsburgh Pittsburgh PA USA. Cancer Immunology and Immunotherapy Program UPMC Hillman Cancer Center Pittsburgh PA USA. Women's Cancer Research Center UPMC Hillman Cancer Center Magee-Womens Research Institute University of Pittsburgh Pittsburgh PA USA. oesterreichs@upmc.edu. Cancer Biology Program UPMC Hillman Cancer Center Pittsburgh PA USA. oesterreichs@upmc.edu. Department of Pharmacology and Chemical Biology University of Pittsburgh Pittsburgh PA USA. oesterreichs@upmc.edu. Department of Immunology University of Pittsburgh School of Medicine Pittsburgh PA USA. dvignali@pitt.edu. Tumor Microenvironment Center UPMC Hillman Cancer Center Pittsburgh PA USA. dvignali@pi

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