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A novel approach to assessing bisphenol-A hazards using an in vitro model system

BMC GENOMICS 2016年第1期17卷 577-577页

作者： Rahman, Md Saidur Kwon, Woo-Sung Yoon, Sung-Jae Park, Yoo-Jin Ryu, Buom-Yong Pang, Myung-Geol Chung Ang Univ Dept Anim Sci & Technol Anseong 456756 Gyeonggi Do South Korea

Background: Although the toxicological impacts of the xenoestrogen bisphenol-A (BPA) have been studied extensively, but the mechanism of action is poorly understood. Eventually, no standard method exists for evaluating the possible health hazards of BPA exposure. Considering mice spermatozoa as a potential in vitro model, we investigated the effects of BPA exposure (0.0001, 0.01, 1, and 100 mu M for 6 h) on spermatozoa and the related mechanisms of action. The same doses were also employed to evaluate protein profiles of spermatozoa as a means to monitor their functional affiliation to diseases. Results: Our results demonstrated that high concentrations of BPA negatively affect sperm motility, viability, mitochondrial functions, and intracellular ATP levels by activating the mitogen-activated protein kinase, phosphatidylinositol 3-kinase, and protein kinase-A pathways. Moreover, short-term exposure of spermatozoa to high concentrations of BPA induced differential expressions of 24 proteins. These effects appeared to be caused by protein degradation and phosphorylation in spermatozoa. Proteins differentially expressed in spermatozoa from BPA treatment groups are putatively involved in the pathogenesis of several diseases, mainly cancer, carcinoma, neoplasm, and infertility. Conclusions: Based on these results, we propose that BPA adversely affects sperm function by the activation of several kinase pathways in spermatozoa. In addition, BPA-induced changes in the sperm proteome might be partly responsible for the observed effects in spermatozoa, subsequently involve in the pathogenesis of many diseases. Therefore, we anticipated that current strategy might broadly consider for the health hazards assessment of other toxicological agents.

关键词： Bisphenol-A Health hazards In-vitro Spermatozoa Proteomics bioinformatics databases Diseases Signaling pathways

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On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types

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BMC bioinformatics 2014年第1期15卷 1-18页

作者： Zhang, Yun Phillips, Charles A. Rogers, Gary L. Baker, Erich J. Chesler, Elissa J. Langston, Michael A. Dupont Pioneer Johnston IA 50131 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Baylor Univ Dept Comp Sci Waco TX 76798 USA Jackson Lab Bar Harbor ME 04609 USA

Background: Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees. Results: The new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives. Conclusions: The new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://***. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practica

关键词： Biclique Biclustering bioinformatics databases Bipartite graphs Graph algorithms

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From biological to social networks: Link prediction based on multi-way spectral clustering

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DATA & KNOWLEDGE ENGINEERING 2013年 87卷 226-242页

作者： Symeonidis, Panagiotis Iakovidou, Nantia Mantas, Nikolaos Manolopoulos, Yannis Aristotle Univ Thessaloniki GR-54006 Thessaloniki Greece Aristotle Univ Thessaloniki Dept Informat GR-54006 Thessaloniki Greece

Link prediction in protein-protein interaction networks (PPINs) is an important task in biology, since the vast majority of biological functions involve such protein interactions. Link prediction is also important for online social networks (OSNs), which provide predictions about who is a friend of whom. Many link prediction methods for PPINs/OSNs are local-based and do not exploit all network structure, which limits prediction accuracy. On the other hand, there are global approaches to detect the overall path structure in a network, being computationally prohibitive for huge-size PPINs/OSNs. In this paper, we enhance a previously proposed multi-way spectral clustering method by introducing new ways to capture node proximity in both PPINs/OSNs. Our new enhanced method uses information obtained from the top few eigenvectors of the normalized Laplacian matrix. As a result, it produces a less noisy matrix, which is smaller and more compact than the original one. In this way, we are able to provide faster and more accurate link predictions. Moreover, our new spectral clustering model is based on the well-known Bray-Curtis coefficient to measure proximity between two nodes. Compared to traditional clustering algorithms, such as k-means and DBSCAN, which assume globular (convex) regions in Euclidean space, our approach is more flexible in capturing the non-connected components of a social graph and a wider range of cluster geometries. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms and k-means algorithm, using two synthetic data sets, three real social networks and three real human protein data sets. Our experimental results show that our SpectralLink algorithm outperforms the local approaches, the k-means algorithm and another spectral clustering method in terms of effectiveness, whereas it is more efficient than the global approaches. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Cross-disciplinary applications Information filtering Clustering bioinformatics databases

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Importance of Different Types of Prior Knowledge in Selecting Genome-Wide Findings for Follow-Up

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GENETIC EPIDEMIOLOGY 2013年第2期37卷 205-213页

作者： Minelli, Cosetta De Grandi, Alessandro Weichenberger, Christian X. Goegele, Martin Modenese, Mirko Attia, John Barrett, Jennifer H. Boehnke, Michael Borsani, Giuseppe Casari, Giorgio Fox, Caroline S. Freina, Thomas Hicks, Andrew A. Marroni, Fabio Parmigiani, Giovanni Pastore, Andrea Pattaro, Cristian Pfeufer, Arne Ruggeri, Fabrizio Schwienbacher, Christine Taliun, Daniel Pramstaller, Peter P. Domingues, Francisco S. Thompson, John R. European Acad Bozen Bolzano EURAC Ctr Biomed Bolzano Italy Univ Newcastle Hunter Med Res Inst Ctr Clin Epidemiol & Biostatist Newcastle NSW 2300 Australia John Hunter Hosp Dept Gen Med Newcastle NSW Australia Univ Leeds Sect Epidemiol & Biostat Leeds Inst Mol Med Leeds W Yorkshire England Univ Michigan Dept Biostat Ann Arbor MI 48109 USA Univ Michigan Ctr Stat Genet Ann Arbor MI 48109 USA Univ Brescia Dept Biomed Sci & Biotechnol Brescia Italy Univ Vita Salute San Raffaele Milan Italy Ctr Translat Genom & Bioinformat Milan Italy NHLBI Framingham Heart Study Framingham MA USA Ctr Populat Studies Framingham MA USA Inst Appl Genom Udine Italy Dana Farber Canc Inst Dept Biostat & Computat Biol Boston MA 02115 USA Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA Ca Foscari Venezia Univ Dept Econ Venice Italy German Res Ctr Environm Hlth GmbH Helmholtz Zentrum Munich Dept Bioinformat & Syst Biol IBIS Neuherberg Germany Inst Appl Math & Informat Technol Natl Res Council Milan Italy Cent Hosp Dept Neurol Bolzano Italy Med Univ Lubeck Dept Neurol D-23538 Lubeck Germany Univ Leicester Dept Hlth Sci Leicester Leics England

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

关键词： gene prioritization genome-wide association studies bioinformatics databases

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Improving Protein-Protein Interaction Pair Ranking with an Integrated Global Association Score

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2012年第6期9卷 1690-1695页

作者： Tsai, Richard Tzong-Han Yuan Ze Univ Dept Comp Sci & Engn Chungli 320 Taiwan

Protein-protein interaction (PPI) database curation requires text-mining systems that can recognize and normalize interactor genes and return a ranked list of PPI pairs for each article. The order of PPI pairs in this list is essential for ease of curation. Most of the current PPI pair ranking approaches rely on association analysis between the two genes in the pair. However, we propose that ranking an extracted PPI pair by considering both the association between the paired genes and each of those genes' global associations with all other genes mentioned in the paper can provide a more reliable ranked list. In this work, we present a composite interaction score that considers not only the association score between two interactors (pair association score) but also their global association scores. We test three representative data fusion algorithms to estimate this global association score-two Borda-Fuse models and one linear combination model (LCM). The three estimation methods are evaluated using the data set of the BioCreative II. 5 Interaction Pair Task (IPT) in terms of area under the interpolated precision/recall curve (AUC iP/R). Our experimental results indicate that using LCM to estimate the global association score can boost the AUC iP/R score from 0.0175 to 0.2396, outperforming the best BioCreative II. 5 IPT system.

关键词： Text mining bioinformatics databases

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Estimating Functional Groups in Human Gut Microbiome With Probabilistic Topic Models

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2012年第3期11卷 203-215页

作者： Chen, Xin He, TingTing Hu, Xiaohua Zhou, Yanhong An, Yuan Wu, Xindong Cent China Normal Univ Dept Comp Sci Wuhan Peoples R China Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Huazhong Univ Sci & Technol Sch Life Sci & Technol Wuhan Peoples R China Univ Vermont Dept Comp Sci Burlington VT USA

In this paper, based on the functional elements derived from non-redundant CDs catalogue, we show that the configuration of functional groups in meta-genome samples can be inferred by probabilistic topic modeling. The probabilistic topic modeling is a Bayesian method that is able to extract useful topical information from unlabeled data. When used to study microbial samples (assuming that relative abundance of functional elements is already obtained by a homology-based approach), each sample can be considered as a "document," which has a mixture of functional groups, while each functional group (also known as a "latent topic") is a weight mixture of functional elements (including taxonomic levels, and indicators of gene orthologous groups and KEGG pathway mappings). The functional elements bear an analogy with "words." Estimating the probabilistic topic model can uncover the configuration of functional groups (the latent topic) in each sample. The experimental results demonstrate the effectiveness of our proposed method.

关键词： bioinformatics databases biological data mining metagenomics probabilistic topic model

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A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2012年第6期9卷 1850-1852页

作者： Campello, Ricardo J. G. B. Moulavi, Davoud Sander, Joerg Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada Univ Sao Paulo Dept Comp Sci Sao Carlos SP Brazil

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

关键词： Data mining clustering bioinformatics databases AUTO-HDS

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bioinformatics advances for clinical biomarker development

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Expert Opinion on Medical Diagnostics 2012年第1期6卷 39-48页

作者： Pritzker, Kenneth P.H. Pritzker, Laura B. Laboratory Medicine and Pathobiology Surgery University of Toronto Mount Sinai Hospital Toronto ON M5G 1X5 600 University Avenue Canada Rna Diagnostics Inc. Toronto ON M5G 2C2 595 Bay Street Canada

bioinformatics tools, techniques and resources are critical to biomarker discovery, assessment, validation, qualification, standardization and market acceptance into clinical practice. Huge scientific effort and economic investment over the past 20 years have resulted in thousands of new candidate biomarkers for diseases, yet relatively few biomarkers have entered clinical practice. bioinformatics is central to all stages of biomarker development and implementation. Areas covered: This review examines bioinformatics advances that bear on each stage of biomarker development and suggests bioinformatics strategies to assist biomarkers towards clinical practice. This paper focuses on the steps of clinical biomarker development with an emphasis on the review literature from 2000 to June 2011. The intent of this paper is to describe the present role of bioinformatics in biomarker development including the controversies associated with various developmental stages. Expert opinion: The key message is that more effective biomarker development requires database input of higher quality, improved bioinformatics tools to identify more clearly the acceptable criteria for each development step, as well as more and better database linkages. © 2012 Informa UK, Ltd.

关键词： bioinformatics bioinformatics databases biomarker biomarker qualification biomarker validation clinical utility comparative effectiveness predictive biomarker prognostic biomarker specimen repository

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Computational knowledge discovery for bioinformatics research /

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2012年

作者： Xiao-Li Li See-Kiong Ng.

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Inferring Functional Groups from Microbial Gene Catalogue with Probabilistic Topic Models

Inferring Functional Groups from Microbial Gene Catalogue wi...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Chen, Xin He, TingTing Hu, Xiaohua An, Yuan Wu, Xindong Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Cent China Normal Univ Dept Comp Sci Wuhan Hubei Peoples R China Univ Vermont Dept Comp Sci Burlington VT USA

ISBN: (纸本)9780769545745

In this paper, based on the functional elements derived from non-redundant CDs catalogue, we show that the configuration of functional groups in meta-genome samples can be inferred by probabilistic topic modeling. The probabilistic topic modeling is a Bayesian method that is able to extract useful topical information from unlabeled data. When used to study microbial samples (assuming that relative abundance of functional elements is already obtained by a homology-based approach), each sample can be considered as a 'document', which has a mixture of functional groups, while each functional group (also known as a 'latent topic') is a weight mixture of functional elements (including taxonomic levels, and indicators of gene orthologous groups and KEGG pathway mappings). The functional elements bear an analogy with 'words'. Estimating the probabilistic topic model can uncover the configuration of functional groups (the latent topic) in each sample. The experimental results demonstrate the effectiveness of our proposed method.

关键词： Biological data mining Metagenomics Probabilistic topic model bioinformatics databases

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