Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.
Cheminformatics refers to the use of physical chemistry theory with computer and information science techniques—so called "in silico" techniques—in application to a range of descriptive and prescriptive problems in the field of chemistry, including in its applications to biology and related molecular fields. Such in silico techniques are used, for example, by pharmaceutical companies and in academic settings to aid and inform the process of drug discovery, for instance in the design of well-defined combinatorial libraries of synthetic compounds, or to assist in structure-based drug design. The methods can also be used in chemical and allied industries, and such fields as environmental science and pharmacology, where chemical processes are involved or studied.
Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.
Biomedical text mining refers to the methods and study of how text mining may be applied to texts and literature of the biomedical domain. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies in this field have been applied to the biomedical literature available through services such as PubMed.
KEGG is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.
This page describes mining for molecules. Since molecules may be represented by molecular graphs this is strongly related to graph mining and structured data mining. The main problem is how to represent molecules while discriminating the data instances. One way to do this is chemical similarity metrics, which has a long tradition in the field of cheminformatics.
Chlorcyclizine is a first-generation antihistamine of the diphenylmethylpiperazine group marketed in the United States and certain other countries. It is used primarily to treat allergy symptoms such as rhinitis, urticaria, and pruritus, and may also be used as an antiemetic. In addition to its antihistamine effects, chlorcyclizine also has some anticholinergic, antiserotonergic, and local anesthetic properties. It also has been studied as a potential treatment for various flaviviruses like Hepatitis C and Zika Virus.
Søren Brunak is a Danish biological and physical scientist working in bioinformatics, systems biology, and medical informatics. He is a professor of Disease Systems Biology at the University of Copenhagen and professor of bioinformatics at the Technical University of Denmark. As Research Director at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen Medical School, he leads a research effort where molecular-level systems biology data are combined with phenotypic data from the healthcare sector, such as electronic patient records, registry information, and biobank questionnaires. A major aim is to understand the network biology basis for time-ordered comorbidities and discriminate between treatment-related disease correlations and other comorbidities in disease trajectories. Søren Brunak also holds a position as a Medical Informatics Officer at Rigshospitalet, the Capital Region of Denmark.
Chemical similarity refers to the similarity of chemical elements, molecules or chemical compounds with respect to either structural or functional qualities, i.e. the effect that the chemical compound has on reaction partners in inorganic or biological settings. Biological effects and thus also similarity of effects are usually quantified using the biological activity of a compound. In general terms, function can be related to the chemical activity of compounds.
ChEMBL or ChEMBLdb is a manually curated chemical database of bioactive molecules with drug inducing properties. It is maintained by the European Bioinformatics Institute (EBI), of the European Molecular Biology Laboratory (EMBL), based at the Wellcome Trust Genome Campus, Hinxton, UK.
Ming-Ming Zhou is an American scientist who focuses on structural and chemical biology, NMR spectroscopy, and drug design. He is the Dr. Harold, Golden Lamport Professor, and Chairman of the Department of Pharmacological Sciences. He is also the Co-Director of the Drug Discovery Institute at the Icahn School of Medicine at Mount Sinai and Mount Sinai Health System in New York City, as well as Professor of Sciences.
Translational bioinformatics (TBI) is a field that emerged in the 2010s to study health informatics, focused on the convergence of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.
In structure mining, a graph kernel is a kernel function that computes an inner product on graphs. Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. They allow kernelized learning algorithms such as support vector machines to work directly on graphs, without having to do feature extraction to transform them to fixed-length, real-valued feature vectors. They find applications in bioinformatics, in chemoinformatics, and in social network analysis.
eTOX is a temporary consortium established in 2010 to share and use toxicology data. It is a pre-competitive collaboration which main goal is to create and distribute tools to predict drug side-effects based on pre-clinical experiments. Aims are a better in silico predictability of potential adverse events and a decrease of the use of animals in toxicological research. eTOX is funded by the Innovative Medicines Initiative (IMI).
Marta Filizola is a computational biophysicist who studies membrane proteins. Filizola's research concerns drug discovery the application of methods of computational chemistry and theoretical chemistry to biochemical and biomedical problems.
Gary Bryce Fogel is an American biologist and computer scientist. He is the Chief Executive Officer of Natural Selection, Inc. He is most known for his applications of computational intelligence and machine learning to bioinformatics, computational biology, and industrial optimization.
AMDD otherwise known as Antimicrobial Drug Database is a biological database that seeks to consolidate antibacterial and antifungal drug information from a variety of sources such as PubChem, PubChem Bioassay, ZINC, ChemDB and DrugBank in order to advance the field of treatment of resistance microbes. As of 2012, AMDD contains ~2900 antibacterial and ~1200 antifungal compounds. These compounds are organized via their description, target, format, bioassay, molecular weight, hydrogen bond donor, hydrogen bond acceptor and rotatable bond. AMDD was built on Apache server 2.2.11. The function of this database is to ultimately provide a comprehensive tool that is intuitively navigated such that development of novel antibacterial and antifungal compounds are facilitated.
Jason W. Locasale is an American scientist and university professor. His focus is on metabolism.
David S. Wishart is a Canadian researcher and a Distinguished University Professor in the Department of Biological Sciences and the Department of Computing Science at the University of Alberta. Wishart also holds cross appointments in the Faculty of Pharmacy and Pharmaceutical Sciences and the Department of Laboratory Medicine and Pathology in the Faculty of Medicine and Dentistry. Additionally, Wishart holds a joint appointment in metabolomics at the Pacific Northwest National Laboratory in Richland, Washington. Wishart is well known for his pioneering contributions to the fields of protein NMR spectroscopy, bioinformatics, cheminformatics and metabolomics. In 2011, Wishart founded the Metabolomics Innovation Centre (TMIC), which is Canada's national metabolomics laboratory.
