Pan Zhang, Bruce R Southey, Sandra L Rodriguez-Zas
{"title":"Co-expression networks uncover regulation of splicing and transcription markers of disease.","authors":"Pan Zhang, Bruce R Southey, Sandra L Rodriguez-Zas","doi":"10.29007/rl4h","DOIUrl":"https://doi.org/10.29007/rl4h","url":null,"abstract":"<p><p>Gene co-expression networks based on gene expression data are usually used to capture biologically significant patterns, enabling the discovery of biomarkers and interpretation of regulatory relationships. However, the coordination of numerous splicing changes within and across genes can exert a substantial impact on the function of these genes. This is particularly impactful in studies of the properties of the nervous system, which can be masked in the networks that only assess the correlation between gene expression levels. A bioinformatics approach was developed to uncover the role of alternative splicing and associated transcriptional networks using RNA-seq profiles. Data from 40 samples, including control and two treatments associated with sensitivity to stimuli across two central nervous system regions that can present differential splicing, were explored. The gene expression and relative isoform levels were integrated into a transcriptome-wide matrix, and then Graphical Lasso was applied to capture the interactions between genes and isoforms. Next, functional enrichment analysis enabled the discovery of pathways dysregulated at the isoform or gene levels and the interpretation of these interactions within a central nervous region. In addition, a Bayesian biclustering strategy was used to reconstruct treatment-specific networks from gene expression profile, allowing the identification of hub molecules and visualization of highly connected modules of isoforms and genes in specific conditions. Our bioinformatics approach can offer comparable insights into the discovery of biomarkers and therapeutic targets for a wide range of diseases and conditions.</p>","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"70 ","pages":"119-128"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764752/pdf/nihms-1577657.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39924445","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":"Radial Basis Function Collocation for the Chemical Master Equation","authors":"Jingwei Zhang, L. Watson, Yang Cao","doi":"10.1142/S0219876210002234","DOIUrl":"https://doi.org/10.1142/S0219876210002234","url":null,"abstract":"The chemical master equation (CME), formulated from the Markov assumption of stochastic processes, offers an accurate description of general chemical reaction systems. This paper proposes a collocation method using radial basis functions to numerically approximate the solution to the CME. Numerical results for some systems biology problems show that the collocation approximation method has good potential for solving large-scale CMEs.","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"19 1","pages":"295-300"},"PeriodicalIF":0.0,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76590325","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":"Gene Regulatory Network Reconstruction Based on Gene Expression and Transcription Factor Activities","authors":"Yao Fu, L. Jarboe, J. Dickerson","doi":"10.7490/F1000RESEARCH.259.1","DOIUrl":"https://doi.org/10.7490/F1000RESEARCH.259.1","url":null,"abstract":"","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"12 1","pages":"113-119"},"PeriodicalIF":0.0,"publicationDate":"2010-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80092161","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":"Network Inference by Considering Multiple Objectives: Insights from In Vivo Transcriptomic Data Generated by a Synthetic Network","authors":"Sandro Lambeck, Andreas Dräger, R. Guthke","doi":"10.15496/publikation-20984","DOIUrl":"https://doi.org/10.15496/publikation-20984","url":null,"abstract":"","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"4 1","pages":"734-742"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84744025","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}
H. Ortiz-Zuazaga, S. P. D. Ortiz, Oscar Moreno de Ayala
{"title":"Error Correction and Clustering Gene Expression Data Using Majority Logic Decoding","authors":"H. Ortiz-Zuazaga, S. P. D. Ortiz, Oscar Moreno de Ayala","doi":"10.6084/M9.FIGSHARE.2056614.V1","DOIUrl":"https://doi.org/10.6084/M9.FIGSHARE.2056614.V1","url":null,"abstract":"","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"39 1","pages":"146-152"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73251350","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":"A New Attempt to Stimulus Related Data Analysis by Structured Neural Networks","authors":"B. Brückner, T. Walter","doi":"10.1007/11893295_29","DOIUrl":"https://doi.org/10.1007/11893295_29","url":null,"abstract":"","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"35 1","pages":"251-259"},"PeriodicalIF":0.0,"publicationDate":"2006-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80007044","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":"Activation Points Extraction and Noise Removal of fMRI Signal Using Novel Local Cosine Technique","authors":"Debebe Asefa, D. Mital, S. Haque, S. Srinivasan","doi":"10.5580/408","DOIUrl":"https://doi.org/10.5580/408","url":null,"abstract":"In this paper we report a novel procedure to accurately estimate the power spectrum of the noise in the fMRI signal at a given voxel location; the estimated power spectrum is used to determine the threshold used as shrinkage or soft threshold to remove noise from both 1-D and 2-D fMRI signal. Spatial processing, such as clustering is done on the entire signal to isolate the BOLD response and further investigate whether the new positions and numbers of the activation points are different from that of theoretically anticipated positions for the experiment performed. It is confirmed that the anticipated positions of the processed fMRI data and the actual positions of the activation points of the original fMRI data coincide as expected theoretically for the experiment performed.","PeriodicalId":93487,"journal":{"name":"Proceedings of the ... annual International Conference on BioInformatics and Computational Biology","volume":"192 1","pages":"721-726"},"PeriodicalIF":0.0,"publicationDate":"2005-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79677943","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}