Journal of Integrative Bioinformatics最新文献_第7页

Integrative illustration of a JCVI-syn3A minimal cell. JCVI-syn3A 最小细胞的综合图示。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2022-06-27 DOI: 10.1515/jib-2022-0013

David S Goodsell

引用次数: 0

Considering best practices in color palettes for molecular visualizations. 考虑分子可视化调色板的最佳实践。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-06-22 DOI: 10.1515/jib-2022-0016

Laura Garrison, Stefan Bruckner

{"title":"Considering best practices in color palettes for molecular visualizations.","authors":"Laura Garrison, Stefan Bruckner","doi":"10.1515/jib-2022-0016","DOIUrl":"https://doi.org/10.1515/jib-2022-0016","url":null,"abstract":"Biomedical illustration and visualization techniques provide a window into complex molecular worlds that are difficult to capture through experimental means alone. Biomedical illustrators frequently employ color to help tell a molecular story, e.g., to identify key molecules in a signaling pathway. Currently, color use for molecules is largely arbitrary and often chosen based on the client, cultural factors, or personal taste. The study of molecular dynamics is relatively young, and some stakeholders argue that color use guidelines would throttle the growth of the field. Instead, content authors have ample creative freedom to choose an aesthetic that, e.g., supports the story they want to tell. However, such creative freedom comes at a price. The color design process is challenging, particularly for those without a background in color theory. The result is a semantically inconsistent color space that reduces the interpretability and effectiveness of molecular visualizations as a whole. Our contribution in this paper is threefold. We first discuss some of the factors that contribute to this array of color palettes. Second, we provide a brief sampling of color palettes used in both industry and research sectors. Lastly, we suggest considerations for developing best practices around color palettes applied to molecular visualization.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40192929","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}

引用次数: 3

Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network 基于多标签深度神经网络的药物化学性质和功能综合分析用于药物不良反应预测

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-05-19 DOI: 10.1515/jib-2022-0007

Pranab Das, Yogita, V. Pal

引用次数: 3

Genome-wide expression reveals potential biomarkers in breast cancer bone metastasis 全基因组表达揭示了乳腺癌症骨转移的潜在生物标志物

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-04-08 DOI: 10.1515/jib-2021-0041

Yashbir Singh, N. Subbarao, Abhinav Jaimini, Quincy A. Hathaway, Amina Kunovac, Bradley J. Erickson, V. Swarup, H. Singh

{"title":"Genome-wide expression reveals potential biomarkers in breast cancer bone metastasis","authors":"Yashbir Singh, N. Subbarao, Abhinav Jaimini, Quincy A. Hathaway, Amina Kunovac, Bradley J. Erickson, V. Swarup, H. Singh","doi":"10.1515/jib-2021-0041","DOIUrl":"https://doi.org/10.1515/jib-2021-0041","url":null,"abstract":"Abstract Breast cancer metastases are most commonly found in bone, an indication of poor prognosis. Pathway-based biomarkers identification may help elucidate the cellular signature of breast cancer metastasis in bone, further characterizing the etiology and promoting new therapeutic approaches. We extracted gene expression profiles from mouse macrophages from the GEO dataset, GSE152795 using the GEO2R webtool. The differentially expressed genes (DEGs) were filtered by log2 fold-change with threshold 1.5 (FDR < 0.05). STRING database and Enrichr were used for GO-term analysis, miRNA and TF analysis associated with DEGs. Autodock Vienna was exploited to investigate interaction of anti-cancer drugs, Actinomycin-D and Adriamycin. Sensitivity and specificity of DEGs was assessed using receiver operating characteristic (ROC) analyses. A total of 61 DEGs, included 27 down-regulated and 34 up-regulated, were found to be significant in breast cancer bone metastasis. Major DEGs were associated with lipid metabolism and immunological response of tumor tissue. Crucial DEGs, Bcl3, ADGRG7, FABP4, VCAN, and IRF4 were regulated by miRNAs, miR-497, miR-574, miR-138 and TFs, CCDN1, STAT6, IRF8. Docking analysis showed that these genes possessed strong binding with the drugs. ROC analysis demonstrated Bcl3 is specific to metastasis. DEGs Bcl3, ADGRG7, FABP4, IRF4, their regulating miRNAs and TFs have strong impact on proliferation and metastasis of breast cancer in bone tissues. In conclusion, present study revealed that DEGs are directly involved in of breast tumor metastasis in bone tissues. Identified genes, miRNAs, and TFs can be possible drug targets that may be used for the therapeutics. However, further experimental validation is necessary.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46059130","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}

引用次数: 0

A review on methods for predicting miRNA–mRNA regulatory modules miRNA-mRNA调控模块预测方法综述

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-04-01 DOI: 10.1515/jib-2020-0048

Madhumita Madhumita, Sushmita Paul

引用次数: 4

ExceS-A: an exon-centric split aligner ExceS-A：一种外显子中心分裂比对器

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-03-01 DOI: 10.1515/jib-2021-0040

Franziska Reinhardt, P. Stadler

引用次数: 0

A systematic study of motif pairs that may facilitate enhancer-promoter interactions. 对促进增强子-启动子相互作用的基序对的系统研究。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-02-07 DOI: 10.1515/jib-2021-0038

Saidi Wang, Haiyan Hu, Xiaoman Li

引用次数: 1

SCARF: a biomedical association rule finding webserver. 一个生物医学关联规则查找web服务器。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-02-04 DOI: 10.1515/jib-2021-0035

Balázs Szalkai, Vince Grolmusz

{"title":"SCARF: a biomedical association rule finding webserver.","authors":"Balázs Szalkai, Vince Grolmusz","doi":"10.1515/jib-2021-0035","DOIUrl":"https://doi.org/10.1515/jib-2021-0035","url":null,"abstract":"The analysis of enormous datasets with missing data entries is a standard task in biological and medical data processing. Large-scale, multi-institution clinical studies are the typical examples of such datasets. These sets make possible the search for multi-parametric relations since from the plenty of the data one is likely to find a satisfying number of subjects with the required parameter ensembles. Specifically, finding combinatorial biomarkers for some given condition also needs a very large dataset to analyze. For fast and automatic multi-parametric relation discovery association-rule finding tools are used for more than two decades in the data-mining community. Here we present the SCARF webserver for generalized association rule mining. Association rules are of the form: a AND b AND … AND x → y, meaning that the presence of properties a AND b AND … AND x implies property y; our algorithm finds generalized association rules, since it also finds logical disjunctions (i.e., ORs) at the left-hand side, allowing the discovery of more complex rules in a more compressed form in the database. This feature also helps reducing the typically very large result-tables of such studies, since allowing ORs in the left-hand side of a single rule could include dozens of classical rules. The capabilities of the SCARF algorithm were demonstrated in mining the Alzheimer's database of the Coalition Against Major Diseases (CAMD) in our recent publication (Archives of Gerontology and Geriatrics Vol. 73, pp. 300-307, 2017). Here we describe the webserver implementation of the algorithm.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39888837","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}

引用次数: 0

Integrative Bioinformatics: History and Future 综合生物信息学:历史与未来

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-6795-4

引用次数: 0

Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data. 基于 ChIP-seq 数据对模式植物基因组中多个转录因子结合情况的统计估算。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2021-12-21 DOI: 10.1515/jib-2020-0036

Arthur I Dergilev, Nina G Orlova, Oxana B Dobrovolskaya, Yuriy L Orlov

{"title":"Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data.","authors":"Arthur I Dergilev, Nina G Orlova, Oxana B Dobrovolskaya, Yuriy L Orlov","doi":"10.1515/jib-2020-0036","DOIUrl":"10.1515/jib-2020-0036","url":null,"abstract":"The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in Arabidopsis thaliana genome were discussed here in detail.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39761184","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}

引用次数: 0