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Clinical Cancer Research : An Official... Nov 2004The ability to parse tumors into subsets based on biomarker expression has many clinical applications; however, there is no global way to visualize the best cut-points...
The ability to parse tumors into subsets based on biomarker expression has many clinical applications; however, there is no global way to visualize the best cut-points for creating such divisions. We have developed a graphical method, the X-tile plot that illustrates the presence of substantial tumor subpopulations and shows the robustness of the relationship between a biomarker and outcome by construction of a two dimensional projection of every possible subpopulation. We validate X-tile plots by examining the expression of several established prognostic markers (human epidermal growth factor receptor-2, estrogen receptor, p53 expression, patient age, tumor size, and node number) in cohorts of breast cancer patients and show how X-tile plots of each marker predict population subsets rooted in the known biology of their expression.
Topics: Adult; Age Factors; Aged; Aged, 80 and over; Biomarkers, Tumor; Breast Neoplasms; Cohort Studies; Computational Biology; ErbB Receptors; Female; Humans; Immunohistochemistry; Lymph Nodes; Middle Aged; Prognosis; Receptors, Estrogen; Software; Tumor Suppressor Protein p53
PubMed: 15534099
DOI: 10.1158/1078-0432.CCR-04-0713 -
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 -
International Journal of Computational... 2012
Topics: Animals; Computational Biology; Humans; Image Processing, Computer-Assisted
PubMed: 22466635
DOI: 10.1504/IJCBDD.2012 -
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 -
Nature Methods Jul 2012For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and...
For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
Topics: Computational Biology; History, 20th Century; History, 21st Century; Image Processing, Computer-Assisted; National Institutes of Health (U.S.); Software; United States
PubMed: 22930834
DOI: 10.1038/nmeth.2089 -
Neuroinformatics Oct 2018
Topics: Computational Biology; Computing Methodologies; Humans; Medical Informatics
PubMed: 30022314
DOI: 10.1007/s12021-018-9393-x -
Drug Discovery Today Jun 2002Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go... (Review)
Review
Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go beyond data- and information-management. Here, we review exemplary areas, such as target identification and validation, virtual screening, and prediction of downstream characteristics of leads, where further research will play a key role in progressing the field.
Topics: Biotechnology; Computational Biology; Drug Discovery; Electronic Data Processing; Forecasting; Humans; Informatics; Information Management
PubMed: 12047879
DOI: 10.1016/s1359-6446(02)02271-7 -
Anticancer Research Sep 2016
Topics: Books; Computational Biology; Humans; Image Processing, Computer-Assisted; Publishing
PubMed: 27630369
DOI: No ID Found -
Journal of Bioscience and Bioengineering Nov 2022Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging... (Review)
Review
Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging analysis. These technologies have made it possible to obtain vast data from a single measurement. Subsequently, large datasets have pioneered the data-driven approach, an alternative to the traditional hypothesis-testing system, for researchers. However, processing, interpreting, and elucidating enormous datasets is no longer possible without computation. Bioinformatics is a field that has developed over long periods, intending to understand biological phenomena using methods collected from information science and statistics, thus solving this proposed research challenge. This review presents the latest methodologies and applications in sequencing, imaging, and mass spectrometry that were developed using bioinformatics. We presented the features of individual techniques and outlines in each part, avoiding the use of complex algorithms and formulas to allow beginning researchers to understand an overview. In the section on sequencing, we focused on comparative genomic, transcriptomic, and bacterial microbiome analyses, which are frequently used as applications of next-generation sequencing. Bioinformatic methods for handling sequence data and case studies were described. In the section on imaging, we introduced the analytical methods and microscopy imaging informatics techniques used in animal cell biology and plant physiology. We introduce informatics technologies for maximizing the value of measured data, including predicting the structure of unknown molecules and untargeted analysis in the section on mass spectrometry. Finally, we discuss the future outlook of this field. We anticipate that this review will assist biologists in using bioinformatics more effectively.
Topics: Animals; Computational Biology; Genomics; High-Throughput Nucleotide Sequencing; Mass Spectrometry; Bioengineering
PubMed: 36127250
DOI: 10.1016/j.jbiosc.2022.08.004 -
Annals of Palliative Medicine Jan 2022The incidence of cerebral hemorrhage has rapidly increased over time, and vascular dysfunction has a significant influence on the pathogenesis and outcome of these...
BACKGROUND
The incidence of cerebral hemorrhage has rapidly increased over time, and vascular dysfunction has a significant influence on the pathogenesis and outcome of these patients. This is also the case for vasospasm in cerebral hemorrhage, but there is no method to assess this. We conducted this study to find molecular biomarkers of vasospasm in cerebral hemorrhage patients.
METHODS
Raw data of GSE37924 was downloaded from the Gene Expression Omnibus (GEO) database, including 66 samples with cerebral vasospasm and 62 samples without cerebral vasospasm. Differentially expressed genes (DEGs) between samples with cerebral vasospasm and those without cerebral vasospasm were analyzed using the limma package in R software. To determine the functions of DEGs, we conducted functional enrichment analysis of DEGs through the clusterProfiler package in R. The protein-protein interaction (PPI) network of DEGs was constructed through STRING (https://string-db.org/) and generated via Cytoscape software. To understand the correlation between DEGs and immune-related genes, immune-related cerebral vasospasm genes were obtained via intersecting immune-related genes and cerebral vasospasm DEGs. We also compared the infiltration of 28 immune cells between cases with cerebral vasospasm and those without cerebral vasospasm. Finally, we constructed a model to perform the validation experiments.
RESULTS
Of the DEGs, there were 24 upregulated and 21 downregulated genes in the vasospasm samples compared to the no-vasospasm samples. Functional enrichment analysis showed that these genes play key roles in several biological processes and signaling pathways such as the bone morphogenetic protein (BMP) signaling pathway, cellular response to BMP stimulus, natural killer cell chemotaxis, negative regulation of transmembrane receptor protein serine/threonine kinase signaling pathway, MHC protein complex binding, and receptor ligand activity, among others. CCL4, HLA-DQA1, IGF2, NTS, and so on were the significant immune-related genes. Furthermore, the immune cell infiltration results showed that there were differences between patients with vasospasm and those without vasospasm. Finally, we found that CCL4 had significantly higher expression in patients with vasospasm than those without vasospasm.
CONCLUSIONS
CCL4 is an important regulator of vascular dysfunction in cerebral hemorrhage.
Topics: Biomarkers; Cerebral Hemorrhage; Computational Biology; Gene Expression Profiling; Humans; Protein Interaction Maps
PubMed: 35144409
DOI: 10.21037/apm-21-3717