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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.
Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.
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
Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution. The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis and modeling. This new standard effectively eliminates the differing methodologies used in studies and creates a unified approach. Readers will learn the advantages and disadvantages of the various techniques discussed, as well as potential difficulties inherent to all steps in the biomarker discovery process. A vital resource for biochemists, biologists, analytical chemists, bioanalytical chemists, clinical and medical technicians, researchers in pharmaceuticals, and graduate students, Proteomic and Metabolomic Approaches to Biomarker Discovery provides the information needed to reduce clinical error in the execution of research. - Describes the use of biomarkers to reduce clinical errors in research - Includes techniques from a range of biomarker discoveries - Covers all steps involved in biomarker discovery, from study design to study execution
This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.
This book focuses on proteomics biomarker discovery and validation procedures from the clinical perspective. It provides an overview of current technology and the challenges encountered throughout the process. This covers all key stages, from biomarker discovery and validation, through to registration with the European and US regulatory authorities (FDA and EMEA). All the important elements (such as patient selection, sample handling, data processing, and statistical analysis) are described in detail and the reader is introduced to each topic with well-described examples or guidelines for best practice. Case studies are also included to demonstrate clinical applications. Individual chapters explain the best performing techniques for profiling complex body fluids and biomarker discovery. This includes the application of mass spectrometry imaging combined with chromatography in profiling platforms and the use of laser micro dissection and MALDI imaging to study tissues in their natural environment. Future developments needed to improve the success rate of translating biomarker discovery into useful clinical tests are also discussed. Common pitfalls and success stories are described as are the limitations of the various technologies involved. Broad and interdisciplinary in approach, this book provides an excellent source of information for industrial and academic researchers, and those managing biobanks.