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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Luo, Kaixuan Hartemink, Alexander J. Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Computer Science Duke University DurhamNC27708 United States

Identifying binding sites of transcription factors (TFs) is a key task in deciphering transcriptional regulation. ChIP-based methods are used to survey the genomic locations of a single TF in each experiment. But methods combining DNase digestion data with TF binding specificity information could potentially be used to survey the locations of many TFs in the same experiment, provided such methods permit reasonable levels of sensitivity and specificity. Here, we present a simple such method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). Our method is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well. Because the number of parameters in our method is at least an order of magnitude smaller than centipede, we dub it millipede.

关键词： Transcription factors

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Statistical measures on residue-level protein structural properties

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Journal of Structural and Functional Genomics 2011年第2期12卷 119-136页

作者： Huang, Yuanyuan Bonett, Stephen Kloczkowski, Andrzej Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50014 United States Summer REU Program on Computational Systems Biology Iowa State University Ames IA 50014 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50014 United States Department of Mathematics Iowa State University Ames IA 50014 United States

The atomic-level structural properties of proteins, such as bond lengths, bond angles, and torsion angles, have been well studied and understood based on either chemistry knowledge or statistical analysis. Similar properties on the residue-level, such as the distances between two residues and the angles formed by short sequences of residues, can be equally important for structural analysis and modeling, but these have not been examined and documented on a similar scale. While these properties are difficult to measure experimentally, they can be statistically estimated in meaningful ways based on their distributions in known proteins structures. Residue-level structural properties including various types of residue distances and angles are estimated statistically. A software package is built to provide direct access to the statistical data for the properties including some important correlations not previously investigated. The distributions of residue distances and angles may vary with varying sequences, but in most cases, are concentrated in some high probability ranges, corresponding to their frequent occurrences in either α-helices or β-sheets. Strong correlations among neighboring residue angles, similar to those between neighboring torsion angles at the atomic-level, are revealed based on their statistical measures. Residue-level statistical potentials can be defined using the statistical distributions and correlations of the residue distances and angles. Ramachandran-like plots for strongly correlated residue angles are plotted and analyzed. Their applications to structural evaluation and refinement are demonstrated. With the increase in both number and quality of known protein structures, many structural properties can be derived from sets of protein structures by statistical analysis and data mining, and these can even be used as a supplement to the experimental data for structure determinations. Indeed, the statistical measures on various types of residue d

关键词： Protein structural analysis Residue-level structural properties Statistical methods Statistical potentials Structural bioinformatics Structural correlation plots Structural databases

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Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae

Cell-cycle phenotyping with conditional random fields: A cas...

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IEEE International Symposium on Biomedical Imaging

作者： Michael B. Mayhew Alexander J. Hartemink Program in Computational Biology & Bioinformatics Duke University Durham NC USA Department of Computer Science Program in Computational Biology & Bioinformatics Duke University Durham NC USA

ISBN: (纸本)9781467364560

High-resolution, multimodal microscopy grants an intimate view of the inner workings of cells. Complex processes like cell division can be monitored with microscope images, assuming identification of cells and their cell-cycle markers: cellular structures indicative of cell-cycle progress. Here, we explore how spatial relationships between these markers can facilitate their identification. We grew and synchronized Saccharomyces cerevisiae cell cultures and then acquired multimodal image data as the cells proceeded through the cell cycle. We trained a conditional random field model to capture pixel-level spatial relationships among three different cell-cycle markers observable in our images. We observed good predictive performance of this pixel-level model on three held-out test images, and performance improved when we used marker-level information from our training data to prune model predictions. Our results support the use of conditional random fields in bioimage labeling and encourage the use of as much multiscale information as available in training data when identifying cell-cycle markers.

关键词： Labeling Predictive models Biological system modeling Training data Microscopy Data models

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Neutral Theory and the Somatic Evolution of Cancer (vol 35, pg 1308, 2018)

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MOLECULAR biology AND EVOLUTION 2018年第6期35卷 1561-1561页

作者： Cannataro, Vincent L. Townsend, Jeffrey P. Department of Biostatistics Yale University New Haven CT Department of Biostatistics Yale University New Haven CT Program in Computational Biology and Bioinformatics Department of Ecology and Evolutionary Biology Yale University New Haven CT Corresponding author: E-mail: jeffrey.townsend@yale.edu. Associate editor

Kimura’s neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution—and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect.

关键词： molecular evolution neutral theory cancer tumor genetics distribution of fitness effects somatic mutation

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Fast and accurate calculation of protein depth by Euclidean distance transform

Fast and accurate calculation of protein depth by Euclidean ...

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17th Annual International Conference on Research in computational Molecular biology, RECOMB 2013

作者： Xu, Dong Li, Hua Zhang, Yang Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute United States Department of Computational Medicine and Bioinformatics University of Michigan United States Integration Application Center Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642371943

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation. © 2013 Springer-Verlag.

关键词： Proteins

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Uncertainty abounds, what now?

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Science 2025年第6740期387卷 1261页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies The reviewer is at the Harry Radzyner Law School The reviewer is at the Dina Recanati School of Medicine Reichman University Herzliya Israel The reviewer is at the Department of Biomedical Informatics and Data Science The reviewer is at the Program in Computational Biology and Bioinformatics The reviewer is at the Department of Computer Science Yale University New Haven CT USA.

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Evaluating the quality of conformation sampling methods using experimental residual dipolar coupling data 11

Evaluating the quality of conformation sampling methods usin...

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

作者： Lin, Tu-Liang Vammi, Santhosh Kumar Song, Guang Computer Science Department Iowa State University Ames IA 50010 United States Program of Bioinformatics and Computational Biology Iowa State University Ames IA 50010 United States

ISBN: (纸本)9781450307963

Many computational approaches have been developed and used for sampling protein conformations near the native state. However, it has been difficult to evaluate the quality of the conformations sampled or to compare them among the various sampling schemes. In this work, we develop a novel method for evaluating the quality of conformation ensembles and apply it to evaluate ubiquitin conformations generated from four widely-used conformation sampling approaches, namely, MD simulation, Elastic Network Model (ENM), CONCOORD, and *** choose ubiquitin because there exists abundant experimental residual dipolar coupling (RDC) data on this protein. RDC data contains rich ensemble-averaged information about a given protein and thus provide tight constraints that can be used for probing what conformations should make up the protein ensemble. Our results demonstrate that the conformations generated by MD simulations are the best among all sampling methods. Specifically, MD simulation performs significantly better than the other methods in capturing the side chain motions. The backbone flexibility modeled and sampled by tCONCOORD comes quite close, with CONCOORD and ENM trailing behind. Copyright © 2011 ACM.

关键词： Conformations

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The bourque distances for mutation trees of cancers 20

The bourque distances for mutation trees of cancers

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20th International Workshop on Algorithms in bioinformatics, WABI 2020

作者： Jahn, Katharina Beerenwinkel, Niko Zhang, Louxin Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland Department of Mathematics and Computational Biology Program National University of Singapore Singapore

ISBN: (纸本)9783959771610

Mutation trees are rooted trees of arbitrary node degree in which each node is labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular RobinsonFoulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients often contain different sets of mutation labels. Here, we generalize the RobinsonFoulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. A connection between the RobinsonFoulds distance and the nearest neighbor interchange distance is also presented. © Katharina Jahn, Niko Beerenwinkel, and Louxin Zhang;licensed under Creative Commons License CC-BY

关键词： Bourque distance Clonal trees Mutation trees Phylogenetic trees Robinson–Foulds distance Tree distance Tree metric

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Investigation of RNA-RNA Interactions Using the RISE Database

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Current Protocols in bioinformatics 2018年第1期64卷 e58/1-e58/1页

作者： Ju, Yanyan Gong, Jing Yang, Yucheng T. Zhang, Qiangfeng Cliff MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology Center for Synthetic and Systems Biology Tsinghua-Peking Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT United States Program in Computational Biology and Bioinformatics Yale University New Haven CT United States

RNA-RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome-wide manner remained challenging until the recent development of several sequencing-based technologies. However, RRIs generated from large-scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome-wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step-by-step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

关键词： interaction RISE RNA RNA structure RNA-RNA sequencing experiments

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Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

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arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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