AI & Medicine Releases Biomarker Discovery and Targeted Proteomics Services
As an expert in the field of AI-Powered drug discovery, personalized healthcare and various medical applications, the New York-based company AI & Medicine recently announces to release biomarker discovery and targeted proteomics services for researchers worldwide. This move is made to meet the increasing demand for new biomarkers in most areas of pathology, especially within the field of cancer diagnostics.
Disease biomarker is a molecule that indicates changes in the physiology of a cell under diseased state and hence can be used as a tool for diagnosis of diseases. During the past decades, biomarker discovery has been mainly focused on the following two basic styles: hypothesis-based or discovery-based. While biomarker identification through hypothesis-based methods is essentially a by-product of the ever-increasing mechanistic understanding of disease processes, the discovery-based approaches focus on identifying changes in the presence or relative abundance of molecular species. It is believed that the combination of liquid biomarker biopsy and AI technology will inevitably improve the accuracy of biomarker discovery.
Based on its AI-Powered drug discovery platform and with the help of numerous data, intelligent tools, bioinformatics and machine learning methods, AI & Medicine now offers biomarker discovery and targeted proteomics services to researchers that want to benefit from its technology.
Proteomics Approaches Compared with the traditional approaches, the in silico approaches are a better choice. Based on re-utilization of pre-existing epidemiologic and genetic data, an in silico framework can be developed and used to identify diseases as indicated by candidate biomarkers. Generally, the following processes are involved: data collection, annotation database creation, open source architecture (Java, PHP, and MySQL) and then discovery of new biomarker.
Data-Driven Biomarker Discovery Literature mining is a key step in this approach. Data extract techniques include lexical (pattern-matching) and linguistic (part-of-speech identification) for unstructured data sources, including Diseases Database, Gene Ontology, Genetic Association Database, KEGG, NCBI Gene Expression Omnibus, OMIM, PubMed, and Medline. Since vast information is embedded in publicly available literature sources and other information databases relevant to specific diseases, comprehensive analysis of these information is a must, which has been conducted by advanced technologies and typically involves with assertional data, intelligence network (IN), criteria search, in silico screening for biomarkers.