BioPlat: a software for human cancer biomarker discovery

ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer

Prostate cancer (PCa) is the second lethal malignancy in men worldwide. In the past, numerous research groups investigated the omics profiles of patients and scrutinized biomarkers for the diagnosis and prognosis of PCa. However, information related to the biomarkers is widely scattered across numerous resources in complex textual format, which poses hindrance to understand the tumorigenesis of this malignancy and scrutinization of robust signature. To create a comprehensive resource, we collected all the relevant literature on PCa biomarkers from the PubMed. We scrutinize the extensive information about each biomarker from a total of 412 unique research articles. Each entry of the database incorporates PubMed ID, biomarker name, biomarker type, biomolecule, source, subjects, validation status, and performance measures such as sensitivity, specificity, and hazard ratio (HR). In this study, we present ProCanBio, a manually curated database that maintains detailed data on 2053 entries of potential PCa biomarkers obtained from 412 publications in user-friendly tabular format. Among them are 766 protein-based, 507 RNA-based, 157 genomic mutations, 260 miRNA-based, and 122 metabolites-based biomarkers. To explore the information in the resource, a web-based interactive platform was developed with searching and browsing facilities. To the best of the authors' knowledge, there is no resource that can consolidate the information contained in all the published literature. Besides this, ProCanBio is freely available and is compatible with most web browsers and devices. Eventually, we anticipate this resource will be highly useful for the research community involved in the area of prostate malignancy.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

A Critical Evaluation of Wet Biomarkers for Huntington's Disease: Current Status and Ways Forward

There is an unmet clinical need for objective biomarkers to monitor disease progression and treatment response in Huntington's disease (HD). The aim of this review is, therefore, to provide practical advice for biomarker discovery and to summarise studies on biofluid markers for HD. A PubMed search was performed to review literature with regard to candidate saliva, urine, blood and cerebrospinal fluid biomarkers for HD. Information has been organised into tables to allow a pragmatic approach to the discussion of the evidence and generation of practical recommendations for future studies. Many of the markers published converge on metabolic and inflammatory pathways, although changes in other analytes representing antioxidant and growth factor pathways have also been found. The most promising markers reflect neuronal and glial degeneration, particularly neurofilament light chain. International collaboration to standardise assays and study protocols, as well as to recruit sufficiently large cohorts, will facilitate future biomarker discovery and development.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.