Bioinformatics Bioinformatics Methods Clinical Bioinformatics

Bioinformatics Methods used in Clinical Research

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Bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen. In Bioinformatics methods used in clinical research, experts observe the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Bioinformatics Methods in Clinical Research made a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

High-throughput genotyping technologies have become popular in studies that aim to reveal the genetics behind polygenic traits such as complex disease and the diverse response to some drug treatments. These technologies utilize bioinformatics tools to define strategies, analyze data, and estimate the final associations between certain genetic markers and traits. The strategy followed for an association study depends on its efficiency and cost. The efficiency is based on the assumed characteristics of the polymorphisms’ allele frequencies and linkage disequilibrium for putative causal alleles. Statistically significant markers that cause a human disorder should be validated and their biological function elucidated. 

Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. 

Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. 

Clinicians and medical researchers believe that healthcare systems would enhance their performance if integrative diagnostic approaches were implemented. Although these approaches are advantageous, they are time-consuming, expensive and require complex interpretation, making it hard to be implemented in clinical laboratories. In the last decade, high-throughput technologies such as NGS, microarrays, RNAseq and MALDI-ToF have been evolving and demonstrating in research studies to have an enormous potential to be applied as integrative approaches in personalized medicine and clinical diagnostics.

With these technologies fully implemented in clinical laboratories, it would be possible to extract a large amount of information about the genome, transcriptome, proteome, metabolome and phenome of patients. The information available from these “omics” data is rich enough to allow screening and early detection of multiple diseases as well as the detection of therapeutic targets in drug discovery. 

Moreover, bioinformatics tools for processing “omics” have also been successful in the discovery of novel drug targets for cancer therapy. Bioinformatics can further improve clinical laboratories efficiency and costs by saving time and human resources on the analysis and reporting to clinics and patients. This can be done by developing pipelines of analysis with automated reporting and APIs fully dedicated to giving real-time online access, facilitating the communication between laboratories, clinicians and patients. Besides, patient historical data and metadata should be secure and organized in a structured way (data “warehouses”) such that it can be further pulled systematically to bioinformatics pipelines. This would allow going beyond in integrative analysis of patients by having their data as a function of time allowing a more personalized monitoring of patients diagnostic and allowing better prognostics.

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