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arXiv 2018年

作者： Warrell, Jonathan Mohsen, Hussein Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University Molecular Biophysics and Biochemistry Yale University Department of Computer Science Yale University

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

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BMC Research Notes 2009年第1期2卷 1-7页

作者： Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 United States Department of Plant Biology Carnegie Institution for Science Stanford CA 94305 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings. This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion. Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. © 2009 Sasidharan et al;licensee BioMed Central Ltd.

关键词： Tiling Array Transcript Mapping Tile Path Tiling Microarray Intergenic Transcript

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Characterizing the tumor microenvironmental changes in a network perspective

Characterizing the tumor microenvironmental changes in a net...

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第四届全国生物信息学与系统生物学学术大会

作者：李文婷王瑞孙之荣 Institute of Bioinformatics School of Life SciencesTsinghua University Computational Biology and Bioinformatics Program Institute for Genome Science and PolicyDuke University Medical CenterDurhamNC27705

Alternations in tumor microenvironment are critical in driving tumor development. However,it proves difficult to characterize their molecular components and further relate to important pathological *** using a network approach,we characterized systematic expression changes at multi-gene module levels between colorectal tumors with and without recurrence after surgery and confirmed their association with two critical

关键词： Characterizing the tumor microenvironmental changes in a network perspective

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Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition (vol 9, 10205, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 1-11页

作者： Mahalapbutr, Panupong Darai, Nitchakan Panman, Wanwisa Opasmahakul, Aunchan Kungwan, Nawee Hannongbua, Supot Rungrotmongkol, Thanyada Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Program in Biotechnology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Department of Chemistry Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand. Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand. Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***. Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***. Molecular Sensory Science Center Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***.

The human T1R2-T1R3 sweet taste receptor (STR) plays an important role in recognizing various low-molecular-weight sweet-tasting sugars and proteins, resulting in the release of intracellular heterotrimeric G protein that in turn leads to the sweet taste perception. Xylitol and sorbitol, which are naturally occurring sugar alcohols (polyols) found in many fruits and vegetables, exhibit the potential caries-reducing effect and are widely used for diabetic patients as low-calorie sweeteners. In the present study, computational tools were applied to investigate the structural details of binary complexes formed between these two polyols and the T1R2-T1R3 heterodimeric STR. Principal component analysis revealed that the Venus flytrap domain (VFD) of T1R2 monomer was adapted by the induced-fit mechanism to accommodate the focused polyols, in which α-helical residues 233-268 moved significantly closer to stabilize ligands. This finding likely suggested that these structural transformations might be the important mechanisms underlying polyols-STR recognitions. The calculated free energies also supported the VFD of T1R2 monomer as the preferential binding site for such polyols, rather than T1R3 region, in accord with the lower number of accessible water molecules in the T1R2 pocket. The E302 amino acid residue in T1R2 was found to be the important recognition residue for polyols binding through a strongly formed hydrogen bond. Additionally, the binding affinity of xylitol toward the T1R2 monomer was significantly higher than that of sorbitol, making it a sweeter tasting molecule.

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A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data

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BMC proceedings 2009年第7期3 Suppl 7卷 S91页

作者： David H Ballard Chatchawit Aporntewan Ji Young Lee Joon Sang Lee Zheyang Wu Hongyu Zhao Program in Computational Biology and Bioinformatics Yale University 300 George Street Suite 503 New Haven Connecticut 06511 USA. david.ballard@yale.edu.

The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.

关键词： Rheumatoid Arthritis Major Histocompatibility Complex Major Histocompatibility Complex Class Binomial Test Gene Score

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Modeling protein interaction network and mechanisms in exocytosis

Modeling protein interaction network and mechanisms in exocy...

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7th IEEE International Conference on bioinformatics and Bioengineering, BIBE

作者： Zhou, Wen Xia, Tian Tong, Jiansong Dickerson, Julie Su, Bo Gu, Xun Department of Mathematics Iowa State University Ames IA 50010 United States Program of Bioinformatics and Computational Biology 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

ISBN: (纸本)1424415098

Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles which contain a wide range of intracellular molecules such as hormones, matrix proteins and neurotransmitters. Recent studies have shown this process is regulated by molecular interactions among a group of well defined and conserved proteins. To gain insight into the dynamics of these interactions, we use protein interaction network modeling to investigate exocytotic system (particularly in endocrine) computationally and mathematically. The protein interactions are formulated into ordinary differential equations (ODE). We then apply a novel mathematic approach to estimate model parameters from experimental data for SNAREs-only network (with three key proteins), which is a subset of this system. Our approach is able to sense temporal changes in protein concentration and ratios of multiple proteins and precisely reconstruct the dynamical process of exocytosis, including vesicle docking, priming and fusion. Additionally, the model suggests that initial concentration of synaptic proteins plays a crucial role in efficiency of vesicle fusion, based on system stability analysis. ©2007 IEEE.

关键词： Proteins

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Distribution-based Sketching of Single-Cell Samples

arXiv

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arXiv 2022年

作者： Baskaran, Vishal Athreya Ranek, Jolene Shan, Siyuan Stanley, Natalie Oliva, Junier B. Department of Computer Science UNC-Chapel Hill United States Computational Medicine Program Curriculum in Bioinformatics and Computational Biology UNC-Chapel Hill United States Computational Medicine Program Department of Computer Science UNC-Chapel Hill United States

Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort. While bioinformatics approaches can be used to link immune cell heterogeneity to external variables of interest, such as, clinical outcome or experimental label, they often struggle to accommodate such a large number of profiled cells. To ease this computational burden, a limited number of cells are typically sketched or subsampled from each patient. However, existing sketching approaches fail to adequately subsample rare cells from rare cell-populations, or fail to preserve the true frequencies of particular immune cell-types. Here, we propose a novel sketching approach based on Kernel Herding that selects a limited subsample of all cells while preserving the underlying frequencies of immune cell-types. We tested our approach on three flow and mass cytometry datasets and on one single-cell RNA sequencing dataset and demonstrate that the sketched cells (1) more accurately represent the overall cellular landscape and (2) facilitate increased performance in downstream analysis tasks, such as classifying patients according to their clinical outcome. An implementation of sketching with Kernel Herding is publicly available at https://***/vishalathreya/Set-Summarization. © 2022, CC0.

关键词： bioinformatics

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AI solutions for drafting in Magic: The gathering

arXiv

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arXiv 2020年

作者： Ward, Henry N. Brooks, Daniel J. Troha, Dan Mills, Bobby Khakhalin, Arseny S. Bioinformatics and Computational Biology Graduate Program University of Minnesota MinneapolisMN United States *** San DiegoCA United States Biology Program Bard College Annandale-on-HudsonNY United States

Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from ***. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Transparent Single-Cell Set Classification with Kernel Mean Embeddings

arXiv

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arXiv 2022年

作者： Shan, Siyuan Baskaran, Vishal Athreya Yi, Haidong Ranek, Jolene Stanley, Natalie Oliva, Junier B. Department of Computer Science UNC Chapel Hill United States Computational Medicine Program Curriculum in Bioinformatics and Computational Biology UNC Chapel Hill United States Computational Medicine Program Department of Computer Science UNC Chapel Hill United States

Modern single-cell flow and mass cytometry technologies measure the expression of several proteins of the individual cells within a blood or tissue sample. Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample’s associated phenotype with machine learning models. Such a large set cardinality also limits the interpretability of machine learning models due to the difficulty in tracking how each individual cell influences the ultimate prediction. We propose using Kernel Mean Embedding to encode the cellular landscape of each profiled biological sample. Although our foremost goal is to make a more transparent model, we find that our method achieves comparable or better accuracies than the state-of-the-art gating-free methods through a simple linear classifier. As a result, our model contains few parameters but still performs similarly to deep learning models with millions of parameters. In contrast with deep learning approaches, the linearity and sub-selection step of our model makes it easy to interpret classification results. Analysis further shows that our method admits rich biological interpretability for linking cellular heterogeneity to clinical phenotype. © 2022, CC0.

关键词： Forecasting

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A comparison of the robustness of evolutionary computation and random walks

A comparison of the robustness of evolutionary computation a...

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Congress on Evolutionary Computation

作者： J. Schonfeld D.A. Ashlock Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Mathematics Department Iowa State University Ames IA USA

Evolution and robustness are thought to be intimately connected. Are solutions to optimization problems produced by evolutionary algorithms more robust to mutation than those produced by other classes of search algorithms? We explore this question in a model system based on bivariate real functions. Bivariate real functions serve as a well understood model systems that are easy to visualize. Both the number and robustness of optimal solutions found in multiple trials with several typical optimization algorithms were compared. In the majority of the function landscapes explored the tournament selection evolutionary algorithm found optimal solutions which were significantly more robust to mutation than those discovered by the other algorithms.

关键词： Robustness Evolutionary computation Genetic mutations Evolution (biology) Biological systems Proteins Stochastic processes Systems engineering and theory Stochastic systems bioinformatics

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