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您的检索式：作者名="Jake Y.Chen"
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| 1 | WIPER: Weighted in-Path Edge Ranking for biomolecular association networks显示文摘Background:In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations.Current bioinformatics research focuses on characterizing candidate genes/proteins,or nodes,with network characteristics,e.g.,betweenness centrality.However,there have been few research reports to characterize and prioritize biomolecular associations('edges'),which can represent gene regulatory events essential to biological processes.Method:We developed Weighted In-Path Edge Ranking(WIPER),a new computational algorithm which can help evaluate all biomolecular interactions/associations('edges')in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result.To validate whether WIPER worked as we designed,we tested the algorithm on synthetic network models.Results:Our results showed WIPER can reliably discover both critical'well traversed in-path edges',which are statistically more traversed than normal edges,and'peripheral in-path edges',which are less traversed than normal edges.Compared with other simple measures such as betweenness centrality,WIPER provides better biological interpretations.In the case study of analyzing postanal pig hearts gene expression,WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation.In the case study of Alzheimer's disease genetic disorder association,WIPER reports SRC:APP,AR:APP,APP:FYN,and APP:NES edges(gene-gene associations)both statistically and biologically important from PubMed co-citation.Conclusion:We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks.Availability:The free WIPER API is described at the website(discovery.informatics.uab.edu/wiper/). | Zongliang Yue Thanh Nguyen Eric Zhang Jianyi Zhang Jake Y.Chen | 2019 | Quantitative Biology2019，7，4： | 1 |
| 2 | Computational Analysis of Drought Stress-Associated miRNAs and miRNA Co-Regulation Network in Physcomitrella patens显示文摘miRNAs are non-coding small RNAs that involve diverse biological processes. Until now, little is known about their roles in plant drought resistance. Physcomitrella patens is highly tolerant to drought; however, it is not clear about the basic biology of the traits that contribute P. patens this important character. In this work, we discovered 16 drought stress-associated miRNA (DsAmR) families in P. patens through computational analysis. Due to the possible discrepancy of expression periods and tissue distributions between potential DsAmRs and their targeting genes, and the existence of false positive results in computational identification, the prediction results should be examined with further experimental validation. We also constructed an miRNA co-regulation network, and identified two network hubs, miR902a-5p and miR414, which may play important roles in regulating drought-resistance traits. We distributed our results through an online database named ppt-miRBase, which can be accessed at http://bioinfor.cnu.edu.cn/ppt_miRBase/index.php. Our methods in finding DsAmR and miRNA co-regulation network showed a new direction for identifying miRNA functions. | Ping Wan Jun WH Yuan Zhou Junshu Xiao Jie Feng Weizhong Zhao Shen Xian Guanglong Jiang Jake Y.Chen | 2011 | Genomics, Proteomics & Bioinformatics2011，9，1： | 1 |
| 3 | Polar Gini Curve:A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data显示文摘In this work,we describe the development of Polar Gini Curve,a method for characterizing cluster markers by analyzing single-cell RNA sequencing(scRNA-seq)data.Polar Gini Curve combines the gene expression and the 2D coordinates(“spatial”)information to detect patterns of uniformity in any clustered cells from scRNA-seq data.We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster,which can be generated during routine scRNA-seq data analysis.To quantify the extent to which a gene is uniformly distributed in a cell cluster space,we combine two polar Gini curves(PGCs)—one drawn upon the cell-points expressing the gene(the“foreground curve”)and the other drawn upon all cell-points in the cluster(the“background curve”).We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster—thus having spatially divergent gene expression patterns within the cluster.Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster—thus having uniform gene expression patterns within the cluster.Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data.We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies.Using this framework to analyze a real-world neonatal mouse heart cell dataset,the detected biomarkers may characterize novel subtypes of cardiac muscle cells.The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/or https://figshare.com/projects/Polar_Gini_Curve/76749. | Thanh Minh Nguyen Jacob John Jeevan Nuo Xu Jake Y.Chen | 2021 | Genomics, Proteomics & Bioinformatics2021，19，3： | 1 |
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