Bioinformatics Analysis Of Omics Data For Biomarker Identification In Clinical Research Volume Ii

Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research, Volume II

This Research Topic is part of a series with, "Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research - Volume I" (https://www.frontiersin.org/research-topics/13816/bioinformatics-analysis-of-omics-data-for-biomarker-identification-in-clinical-research) The advances and the decreasing cost of omics data enable profiling of disease molecular features at different levels, including bulk tissues, animal models, and single cells. Large volumes of omics data enhance the ability to search for information for preclinical study and provide the opportunity to leverage them to understand disease mechanisms, identify molecular targets for therapy, and detect biomarkers of treatment response. Identification of stable, predictive, and interpretable biomarkers is a significant step towards personalized medicine and therapy. Omics data from genomics, transcriptomics, proteomics, epigenomics, metagenomics, and metabolomics help to determine biomarkers for prognostic and diagnostic applications. Preprocessing of omics data is of vital importance as it aims to eliminate systematic experimental bias and technical variation while preserving biological variation. Dozens of normalization methods for correcting experimental variation and bias in omics data have been developed during the last two decades, while only a few consider the skewness between different sample states, such as the extensive over-repression of genes in cancers. The choice of normalization methods determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or preprocessing strategies may promote biomarker identification and facilitate clinical decision-making.

Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research

The Hidden World of Protein Aggregation

The Hidden World of Protein Aggregation, Volume 206 provides a comprehensive exploration of protein aggregation, uncovering the factors behind the formation of amorphous aggregates and ordered structures called amyloid fibrils. It delves into the advantages and disadvantages of protein aggregates, addressing topics such as cytotoxicity and disorders linked to misfolding. Specific chapters in this release include Protein Aggregation: An Overview, Pathways of Amyloid Fibril Formation and Aggregation, Factors Influencing Amyloid Fibril Formation, Morphological Features and Types of Aggregated Structures, Each big journey starts with a first step: Importance of Oligomerization, Liquid-Liquid Phase Separation as Triggering Factor of Fibril Formation, and more.Additional sections cover Experimental Techniques for Detecting and Evaluating the Amyloid Fibrils, Prediction of Protein Aggregation, Amyloid Fibril Cytotoxicity and Associated Disorders, Inhibitors of Amyloid Fibril Formation, Therapeutic Approaches in Proteinopathies, Functional Amyloids, Biotechnological Applications of Amyloid Fibrils, and The Hidden World of Protein Aggregation. - Provides an introduction to the folding of protein and associated conditions leading to aggregation and linked pathology - Discusses structural biology and computational methodologies for analysis of protein (mis)folding and aggregation - Describes functional amyloids and their biotechnological applications