检索结果-内蒙古大学图书馆

5th International Conference on bioinformatics and computational biology 2013, BICoB 2013

作者： Ramadan, Emad Perincheri, Sudhir Sprecher, Emmett Harris, Lyndsay Tuck, David Information and Computer Science Department King Fahd University Dhahran Saudi Arabia Department of Pathology Yale University New Haven United States Computational Biology and Bioinformatics Program Yale University New Haven United States Division of Hematology and Oncology Case Western Reserve University Cleveland United States

ISBN: (纸本)9781622769711

Background: Understanding crosstalk and feedback among oncogenic pathways is a critical challenge to overcoming resistance to targeted anticancer therapy. The topology of biological networks has increasingly been used to complement studies based on individual genes or gene collections. We apply network analysis to understand interactions among critical biological signaling pathways in breast cancer. Results: An overlapping clustering of the networks showed highly distinct patterns in patients with high IGF versus low IGF axis. We demonstrate through cluster comparison metrics and permutation studies that these differences are unlikely to occur by chance. IRS-1, an IGF adaptor protein, is shown to have a highly central place in the IGF high as compared to the IGF low networks. We further demonstrate that network connections reveal information about interactions of genes in TGF-beta, MAPK, and other pathways known to interact with IGF. Conclusions: Network analyses can provide novel insights and hypotheses about signaling pathways involved in feedback and crosstalk in breast cancer.

关键词： Crosstalk

来源：评论

学校读者我要写书评

暂无评论

Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets

arXiv

引用

arXiv 2024年

作者： Landa, Boris Kluger, Yuval Ma, Rong Department of Electrical Engineering Yale University United States Program in Applied Mathematics Yale University United States Department of Biostatistics Harvard University United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University United States Department of Pathology Yale University School of Medicine United States

Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed multiple datasets from different studies or experimental conditions. Such datasets may share underlying structures of interest but exhibit individual distortions, resulting in misaligned embeddings using traditional techniques. In this work, we propose Entropic Optimal Transport (EOT) eigenmaps, a principled approach for aligning and jointly embedding a pair of datasets with theoretical guarantees. Our approach leverages the leading singular vectors of the EOT plan matrix between two datasets to extract their shared underlying structure and align the datasets accordingly in a common embedding space. We interpret our approach as an inter-data variant of the classical Laplacian eigenmaps and diffusion maps embeddings, showing that it enjoys many favorable analogous properties. We then analyze a data-generative model where two observed high-dimensional datasets share latent variables on a common low-dimensional manifold, but each dataset is subject to data-specific translation, scaling, nuisance structures, and noise. We show that in a high-dimensional asymptotic regime, the EOT plan recovers the shared manifold structure by approximating a kernel function evaluated at the locations of the latent variables. Subsequently, we provide a geometric interpretation of our embedding by relating it to the eigenfunctions of population-level operators encoding the density and geometry of the shared manifold. Finally, we showcase the performance of our approach for data integration and embedding through simulations and analyses of real-world biological data, demonstrating its advantages over alternative methods in challenging scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Text mining systems biology: Turning the microscope back on the observer

引用

Current Opinion in Systems biology 2018年 11卷 117-122页

作者： Kong, Xiangmeng Gerstein, Mark B. Program in Computational Biology & Bioinformatics Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Molecular Biophysics & Biochemistry Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Computer Science Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States

In this review, we describe the relationship between systems biology and text mining. On the one hand, text mining functions as a practical tool for systems biology research, which integrates diverse sub-fields and takes an overall, systems-level view of biological phenomena. In this vein, various analyses have been done to recognize biological entities, construct networks of them (e.g. protein–protein interaction maps) and even find disease-associated genes directly from texts. On the other hand, text mining can also be applied to study systems biology itself, giving a ”distant-reading perspective” on the evolution of the field. For example, by examining changes in the frequencies of terms in systems biology publications, we can analyze trends in research focus and in the popularity of systems approaches in various subdomains (e.g., autism). Given these two uses of text mining for systems biology, we close with suggestions for adapting current publication formats for facilitating text mining and enabling its broader use. © 2018

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

引用

BMC proceedings 2009年第7-Sup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

来源：评论

学校读者我要写书评

暂无评论

Erratum to: An approach to compare genome tiling microarray and MPSS sequencing data for transcript mapping

引用

BMC Research Notes 2009年第1期2卷 1-2页

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

We are correcting the abstract of our published article ([1]). The sentence that starts "We observe that 4.5% of MPSS tags...." was not scientifically complete in the original abstract, having only two of the four numbers required to describe a comparison of two technologies in two different organisms. The abstract below more accurately describes our findings, as documented in Figure 1 of the manuscript.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dynamic and static analysis of transcriptional regulatory networks in a hierarchical context

Dynamic and static analysis of transcriptional regulatory ne...

引用

IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Nitin Bhardwaj Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Heaven CT USA Department of Computer Science Yale University New Heaven CT USA

Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures such as hierarchies. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. Here we study a wide range of regulatory networks (transcriptional, modification and phosphorylation) in a hierarchical context for five evolutionarily diverse species. We specify three levels of regulators - top, middle and bottom - which collectively regulate the non-regulator targets lying in the lowest fourth level, and we define quantities for nodes, levels and entire networks that measure their degree of collaboration and autocratic or democratic character. Overall we show that in all the networks studied, the middle level has the highest collaborative propensity and that co-regulatory partnerships occur most frequently amongst mid-level regulators, an observation that has parallels in efficient corporate settings where middle managers need to interact most to ensure organizational effectiveness. Then to study dynamic effects, superimpose the phenotypic effects of tampering with nodes and edges directly onto the hierarchies. We reconstruct modified hierarchies reflecting changes in the level of regulators within the hierarchy upon deletions or insertions of nodes or edges. Overall, we find that rewiring events that affect upper levels have a more dramatic effect on cell proliferation rate and survival than do those involving lower levels. We also investigate other features connected to the importance of upper-level regulators: expression divergence, back-up copies and expression level.

关键词： Regulators Collaboration Context bioinformatics Biochemistry Molecular biophysics Performance analysis

来源：评论

学校读者我要写书评

暂无评论

Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease

引用

Quantitative biology 2021年第2期9卷 216-227页

作者： Mo Li Xue Zeng Chentian Jin Sheng Chih Jin Weilai Dong Martina Brueckner Richard Lifton Qiongshi Lu Hongyu Zhao Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Department of Molecular Cellular&Developmental BiologyYale UniversityCT 06510USA Department of Genetics Washington University School of MedicineSt LouisMO 63110USA Department of Pediatrics Yale UniversityNew HavenCT 06510USA Laboratory of Human Genetics and Genomics Rockefeller UniversityNew YorkNY 10065USA Department of Biostatistics and Medical Informatics University of Wisconsin-MadisonMadisonWl 53792USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Background:Whole-exome sequencing(WES)studies have identified multiple genes enriched for de novo mutations(DNMs)in congenital heart disease(CHD)***,risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each ***:In this manuscript,we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted *** integrative modeling of multiple types of genetic variants,gene-level annotations,and reference data from large population cohorts,our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs ***:Applied to WES data of 2,645 CHD proband-parent trios,our method identified 15 significant genes,half of which are novel,leading to new insights into the genetic bases of ***:These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.

关键词： rare variants gene-level association test congenital heart disease de novo mutation

来源：评论

学校读者我要写书评

暂无评论

Higher-Order Generalization Bounds: Learning Deep Probabilistic programs via PAC-Bayes Objectives

arXiv

引用

arXiv 2022年

作者： Warrell, Jonathan Gerstein, Mark Program in Computational Biology and Bioinformatics Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States

Deep Probabilistic programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP offers a unified perspective, where inference and learning algorithms are treated on a par with models as stochastic programs. Here, we offer a framework for representing and learning flexible PAC-Bayes bounds as stochastic programs using DPP-based methods. In particular, we show that DPP techniques may be leveraged to derive generalization bounds that draw on the compositionality of DPP representations. In turn, the bounds we introduce offer principled training objectives for higher-order probabilistic programs. We offer a definition of a higher-order generalization bound, which naturally encompasses single- and multi-task generalization perspectives (including transfer- and meta-learning) and a novel class of bound based on a learned measure of model complexity. Further, we show how modified forms of all higher-order bounds can be efficiently optimized as objectives for DPP training, using variational techniques. We test our framework using single- and multi-task generalization settings on synthetic and biological data, showing improved performance and generalization prediction using flexible DPP model representations and learned complexity measures. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Special issue on genome wide association study

引用

Quantitative biology 2021年第2期9卷 105-106页

作者： Lin Hou Qiongshi Lu Can Yang Hongyu Zhao Center for Statistical Science Department of Industrial EngineeringMOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing 100084China Department of Biostatistics and Medical Informatics Department of StatisticsCenter for Demography of Health and AgingUniversity of Wisconsin-MadisonWl 53726USA Department of Mathematics the Hong Kong University of Science and TechnologyHong KongChina Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 *** is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through ***,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS *** rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and *** special issue of Quantitative biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.

关键词： hundreds GWAS initiated

来源：评论

学校读者我要写书评

暂无评论

Geometry based data generation 18

Geometry based data generation

引用

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Ofir Lindenbaum Jay S. Stanley, III Guy Wolf Smita Krishnaswamy Applied Mathematics Program Yale University New Haven CT Computational Biology & Bioinformatics Program Yale University New Haven CT Departments of Genetics & Computer Science Yale University New Haven CT

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：