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Bioscience Reports Aug 2017Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational... (Review)
Review
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
Topics: Animals; Computational Biology; Drug Discovery; Humans
PubMed: 28487472
DOI: 10.1042/BSR20160180 -
Genome Biology May 2017High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for... (Review)
Review
High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of "integrative genetics". Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.
Topics: Computational Biology; Datasets as Topic; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans
PubMed: 28476144
DOI: 10.1186/s13059-017-1215-1 -
Journal of B.U.ON. : Official Journal... 2017Bioinformatics is one of the newest fields of biological research, and should be viewed broadly as the use of mathematical, statistical, and computational methods for... (Review)
Review
Bioinformatics is one of the newest fields of biological research, and should be viewed broadly as the use of mathematical, statistical, and computational methods for the processing and analysis of biological data. Over the last decade, the rapid growth of information and technology in both "genomics" and "omics" eras has been overwhelming for the laboratory scientists to process experimental results. Traditional gene-by-gene approaches in research are insufficient to meet the growth and demand of biological research in understanding the true biology. The massive amounts of data generated by new technologies as genomic sequencing and microarray chips make the management of data and the integration of multiple platforms of high importance; this is then followed by data analysis and interpretation to achieve biological understanding and therapeutic progress. Global views of analyzing the magnitude of information are necessary and traditional approaches to lab work have steadily been changing towards a bioinformatics era. Research is moving from being restricted to a laboratory environment to working with computers in a "virtual lab" environment. The present review article shall put light on this emerging field and its applicability towards cancer research.
Topics: Computational Biology; Humans; Microarray Analysis; Neoplasms; Proteomics
PubMed: 29155508
DOI: No ID Found -
Experimental & Molecular Medicine Sep 2020Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently,... (Review)
Review
Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.
Topics: Animals; Biotechnology; Computational Biology; Epigenomics; Gene Expression Profiling; Genomics; Humans; Organ Specificity; Proteomics; Single-Cell Analysis; Transcriptome
PubMed: 32929225
DOI: 10.1038/s12276-020-0420-2 -
BMC Bioinformatics Dec 2020This is an editorial report of the supplements to BMC Bioinformatics that includes 6 papers selected from the BIOCOMP'19-The 2019 International Conference on...
This is an editorial report of the supplements to BMC Bioinformatics that includes 6 papers selected from the BIOCOMP'19-The 2019 International Conference on Bioinformatics and Computational Biology. These articles reflect current trend and development in bioinformatics research.
Topics: Computational Biology; Genomics; Humans; Magnetic Resonance Spectroscopy; Neoplasm Proteins; Neoplasms; Research
PubMed: 33272214
DOI: 10.1186/s12859-020-03874-y -
PLoS Biology Mar 2021Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of...
Why would a computational biologist with 40 years of research experience say bioinformatics is dead? The short answer is, in being the Founding Dean of a new School of Data Science, what we do suddenly looks different.
Topics: Computational Biology; Curriculum; Data Science; Humans; Information Dissemination; Schools; Students
PubMed: 33735179
DOI: 10.1371/journal.pbio.3001165 -
Analytica Chimica Acta Jan 2021Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA... (Review)
Review
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
Topics: Biomedical Research; Computational Biology; Genomics; Metabolomics; Phenotype
PubMed: 33248648
DOI: 10.1016/j.aca.2020.10.038 -
Genes Apr 2020In recent years, technology breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology [...].
In recent years, technology breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology [...].
Topics: Computational Biology; Genome, Human; Genomics; Humans; Statistics as Topic
PubMed: 32325634
DOI: 10.3390/genes11040443 -
Genes Apr 2020The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an...
The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in . The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.
Topics: Algorithms; Biomedical Research; Computational Biology; Humans; Medicine; Systems Biology; Transcriptome
PubMed: 32316483
DOI: 10.3390/genes11040437 -
BioMed Research International 2015
Topics: Biochemistry; Biomedical Research; Computational Biology; Humans; Systems Biology
PubMed: 25961026
DOI: 10.1155/2015/568607