American journal of bioinformatics and computational biology最新文献

{"title":"KNGP: A network-based gene prioritization algorithm that incorporates multiple sources of knowledge.","authors":"Chad Kimmel,&nbsp;Shyam Visweswaran","doi":"10.7726/ajbcb.2015.1001","DOIUrl":"https://doi.org/10.7726/ajbcb.2015.1001","url":null,"abstract":"<p><strong>Background: </strong>Candidate gene prioritization is the process of identifying and ranking new genes as potential candidates of being associated with a disease or phenotype. Integrating multiple sources of biological knowledge for gene prioritization can improve performance.</p><p><strong>Results: </strong>We developed a novel network-based gene prioritization algorithm called Knowledge Network Gene Prioritization (KNGP) that can incorporate node weights in addition to the usually used link weights. The online Web implementation of KNGP can handle small input files while the downloadable R software package can handle larger input files. We also provide several files of coded biological knowledge that can be used by KNGP.</p>","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"3 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.7726/ajbcb.2015.1001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37367051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3DProIN: Protein-Protein Interaction Networks and Structure Visualization.","authors":"Hui Li,&nbsp;Chunmei Liu","doi":"10.7726/ajbcb.2014.1003","DOIUrl":"https://doi.org/10.7726/ajbcb.2014.1003","url":null,"abstract":"<p><p>3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.</p>","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"2 1","pages":"32-37"},"PeriodicalIF":0.0,"publicationDate":"2014-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4316736/pdf/nihms654175.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33038962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 24th International Conference on genome Informatics, Giw2013, in Singapore","authors":"F. Eisenhaber, W. Sung, L. Wong","doi":"10.1142/S0219720013020034","DOIUrl":"https://doi.org/10.1142/S0219720013020034","url":null,"abstract":"","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80085558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Introduction to some New Results in Bioinformatics and Computational Biology","authors":"L. Wong","doi":"10.1142/S0219720013010014","DOIUrl":"https://doi.org/10.1142/S0219720013010014","url":null,"abstract":"describes an algorithm and its associated software, MinimalMarker, forproducing a minimal set of DNA markers for characterizing a given set of crops. Theprogram can be used with both dominant and co-dominant markers regardless of thenumber of alleles, including short sequence repeats.Discovering the linkage and association of a gene to a disease has been an activearea of research.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79635991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction: Advances in Computational Systems Bioinformatics","authors":"T. Wittkop, S. Mooney","doi":"10.1142/S0219720011005537","DOIUrl":"https://doi.org/10.1142/S0219720011005537","url":null,"abstract":"This special issue of the Journal of Bioinformatics and Computational Biology is devoted to the Computational Systems Bioinformatics Conference (CSB) held in August 2010 at Stanford University. Out of 19 peer-reviewed manuscripts that have been presented at the conference and subsequently been published in the Proceedings of the Nineth Computational Systems Bioinformatics Conference (CSB’10), we selected eight papers to be published here. The selected papers have reached the highest scores in the initial peer-reviewing process. As we offered the authors to expand the conference manuscript by up to 30%, a second review process has been conducted to strengthen their scientific quality. The final result is this exciting collection of eight high-quality papers: Parker et al. present in “Optimization of therapeutic proteins to delete T-Cell epitopes while maintaining beneficial residue interactions” an integer programming approach that attacks the NP-hard problem of selecting sets of mutations predicted to delete immunogenic T-cell epitopes, while simultaneously maintaining important residues and residue interactions. In “Temporal graphical models for cross-species gene regulatory network discovery”, Liu et al. concentrated on cross-species gene expression analysis. They developed a hidden Markov random field regression to jointly uncover the regulatory networks for multiple species, thus capturing the causal relations between genes from time-series microarray data across species. In their paper “Classification of large microarray datasets using fast random forest construction”, Manilich et al. customized the widely used random forest classifier to address specific properties of microarray data. By reducing overlapping computations and eliminating dependency on the size of the main memory, their implementation shows an increased performance for this application. Ozer et al. studied ways to compare multiple ChIP-seq experiments in their manuscript “Comparing multiple ChIP-sequencing experiments” in order to attack the challenge of comparing multiple cell lines under different experimental conditions despite the massive amount of data produced by high-throughput sequencing","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80744582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Animal microRNA Target Prediction Using Diverse Sequence-Specific determinants","authors":"Yun Zheng, Weixiong Zhang","doi":"10.1142/S0219720010004896","DOIUrl":"https://doi.org/10.1142/S0219720010004896","url":null,"abstract":"Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions ...","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"148 1","pages":"763-788"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90091222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additive Risk Analysis of microarray Gene Expression Data via Correlation Principal Component Regression","authors":"Yichuan Zhao, Guoshen Wang","doi":"10.1142/S0219720010004914","DOIUrl":"https://doi.org/10.1142/S0219720010004914","url":null,"abstract":"In order to predict future patients' survival time based on their microarray gene expression data, one interesting question is how to relate genes to survival outcomes. In this paper, by applying a semi-parametric additive risk model in survival analysis, we propose a new approach to conduct a careful analysis of gene expression data with the focus on the model's predictive ability. In the proposed method, we apply the correlation principal component regression to deal with right censoring survival data under the semi-parametric additive risk model frame with high-dimensional covariates. We also employ the time-dependent area under the receiver operating characteristic curve and root mean squared error for prediction to assess how well the model can predict the survival time. Furthermore, the proposed method is able to identify significant genes, which are significantly related to the disease. Finally, the proposed useful approach is illustrated by the diffuse large B-cell lymphoma data set and breast cancer data set. The results show that the model fits the data sets very well.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"82 3 1","pages":"645-659"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88049457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global metal-ion Binding protein Fingerprint: a Method to Identify Motif-Less metal-ion Binding proteins","authors":"Abhilash Mohan, Sharmila Anishetty, P. Gautam","doi":"10.1142/S0219720010004884","DOIUrl":"https://doi.org/10.1142/S0219720010004884","url":null,"abstract":"Metal-ion binding proteins play a vital role in biological processes. Identifying putative metal-ion binding proteins is through knowledge-based methods. These involve the identification of specific motifs that characterize a specific class of metal-ion binding protein. Metal-ion binding motifs have been identified for the common metal ions. A robust global fingerprint that is useful in identifying a metal-ion binding protein from a non-metal-ion binding protein has not been devised. Such a method will help in identifying novel metal-ion binding proteins and proteins that do not possess a canonical metal-ion binding motif. We have used a set of physico-chemical parameters of metal-ion binding proteins encoded by the genes CzcA, CzcB and CzcD as a training set to supervised classifiers and have been able to identify several other metal ion binding proteins leading us to believe that metal-ion binding proteins have a global fingerprint, which cannot be pinned down to a single feature of the protein sequence.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"26 1","pages":"717-726"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81054791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brief Introduction to Some New Results in Gene Expression Analysis, Systems Biology Modeling, Motif Identification, and (noncoding) RNA Analysis","authors":"L. Wong","doi":"10.1142/S0219720010005026","DOIUrl":"https://doi.org/10.1142/S0219720010005026","url":null,"abstract":"This issue of the Journal of Bioinformatics and Computational Biology presents a number of new approaches to several key problems in the analysis of data from biological experiments. These approaches are briefly summarized below. The possibility of using gene expression profiling by microarrays for diagnostic and prognostic purposes has generated much excitement and research for the last 10 years. Nevertheless, a number of issues persist such as how to identify genes that are meaningful in explaining the difference in response and phenotypes, how to rectify batch effects, and how to deal with censoring when modeling survival outcome. In this issue, Zhao and Wang apply correlation principal component regression to handle censoring in survival data under a semi-parametric additive risk model to identify genes which are significantly related to patient survival of a disease. Their technique is shown to be effective on several datasets and is comparatively convenient to implement. The study of genetic regulatory systems has also received a major impetus from microarray technology, which permits the spatial-temporal expression levels of genes to be measured in a massively parallel way. In particular, there is keen interest in inferring regulatory networks from gene expression data. In this issue, Kimura et al. consider a method based on linear programming machines for inferring gene regulation and discuss an approach for assigning confidence values to the inferred gene regulation. This approach is shown to reduce false-positive regulations in a simulated experiment and to produce reasonable confidence values in a real experiment. On the other hand, Fujita et al. attempt to infer Granger causality between sets of genes. They propose a method for identification of Granger causality and a statistical test based on nonparametric bootstrapping. The effectiveness of this approach is demonstrated by simulations and by a successful application to actual biological gene expression data. Metal ions have a major effect on the metabolic processes. For example, zinc (II) ions have been linked to inhibition of Krebs cycle and alteration of energy metabolism. Several distinct mechanisms for zinc (II) inhibition of Krebs cycle have been proposed. In this issue, Čuperlović-Culf constructs a mathematical model of","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87141499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fpqrna: Hardware-Accelerated Qrna Package for noncoding RNA Gene Detecting on FPGA","authors":"Fei Xia, Y. Dou, Guo-Qing Lei","doi":"10.1142/S0219720010004902","DOIUrl":"https://doi.org/10.1142/S0219720010004902","url":null,"abstract":"Noncoding RNAs (ncRNAs) have important functional roles in biological processes and have become a central research interest in modern molecular biology. However, how to find ncRNA attracts much more attention since ncRNA gene sequences do not have strong statistical signals, unlike protein coding genes. QRNA is a powerful program and has been widely used as an efficient analysis tool to detect ncRNA gene at present. Unfortunately, the O(L3) computing requirements and complicated data dependency greatly limit the usefulness of QRNA package with the explosion in gene database. In this paper, we present a fine-grained parallel QRNA prototype system, FPQRNA, for accelerating ncRNA gene detection application on FPGA chip. We propose a systolic-like array architecture with multiple PEs (Processing Elements). We partition the tasks by columns and assign tasks to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrices from external memory. The experimental results show a speedup factor of more than 18× over the QRNA - 2.0.3c software running on a PC platform with AMD Phenom 9650 Quad CPU for pairwise sequence alignment with 996 residues, however the power consumption of our FPGA accelerator is only about 30% of that of the general-purpose microprocessors.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"69 1","pages":"743-761"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79554605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}