Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, chemistry, physics, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for in silico analyses of biological queries using computational and statistical techniques.
Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer engineering which uses bioengineering to build computers.
Health informatics is the field of science and engineering that aims at developing methods and technologies for the acquisition, processing, and study of patient data, which can come from different sources and modalities, such as electronic health records, diagnostic test results, medical scans. The health domain provides an extremely wide variety of problems that can be tackled using computational techniques.
Record linkage is the task of finding records in a data set that refer to the same entity across different data sources. Record linkage is necessary when joining different data sets based on entities that may or may not share a common identifier, which may be due to differences in record shape, storage location, or curator style or preference. A data set that has undergone RL-oriented reconciliation may be referred to as being cross-linked.
An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.
Biomedical text mining refers to the methods and study of how text mining may be applied to texts and literature of the biomedical and molecular biology domains. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies developed through studies in this field are frequently applied to the biomedical and molecular biology literature available through services such as PubMed.
Public health informatics has been defined as the systematic application of information and computer science and technology to public health practice, research, and learning. It is one of the subdomains of health informatics.
Mark Bender Gerstein is an American scientist working in bioinformatics and Data Science. As of 2009, he is co-director of the Yale Computational Biology and Bioinformatics program.
Personal genomics or consumer genetics is the branch of genomics concerned with the sequencing, analysis and interpretation of the genome of an individual. The genotyping stage employs different techniques, including single-nucleotide polymorphism (SNP) analysis chips, or partial or full genome sequencing. Once the genotypes are known, the individual's variations can be compared with the published literature to determine likelihood of trait expression, ancestry inference and disease risk.
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with large body of information that we could not comprehend when used only in smaller amounts.
Infoveillance is a type of syndromic surveillance that specifically utilizes information found online. The term, along with the term infodemiology, was coined by Gunther Eysenbach to describe research that uses online information to gather information about human behavior.
Translational bioinformatics (TBI) is an emerging field in the study of 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.
The social genome is the collection of data about members of a society that is captured in ever-larger and ever-more complex databases. Some have used the term digital footprint to refer to individual traces.
Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition.
Data re-identification or de-anonymization is the practice of matching anonymous data with publicly available information, or auxiliary data, in order to discover the individual to which the data belong. This is a concern because companies with privacy policies, health care providers, and financial institutions may release the data they collect after the data has gone through the de-identification process.
DNA encryption is the process of hiding or perplexing genetic information by a computational method in order to improve genetic privacy in DNA sequencing processes. The human genome is complex and long, but it is very possible to interpret important, and identifying, information from smaller variabilities, rather than reading the entire genome. A whole human genome is a string of 3.2 billion base paired nucleotides, the building blocks of life, but between individuals the genetic variation differs only by 0.5%, an important 0.5% that accounts for all of human diversity, the pathology of different diseases, and ancestral story. Emerging strategies incorporate different methods, such as randomization algorithms and cryptographic approaches, to de-identify the genetic sequence from the individual, and fundamentally, isolate only the necessary information while protecting the rest of the genome from unnecessary inquiry. The priority now is to ascertain which methods are robust, and how policy should ensure the ongoing protection of genetic privacy.
Lucila Ohno-Machado is a biomedical engineer and the chair of the Department of Biomedical Informatics and associate dean for informatics and technology at UC San Diego. She is an elected member of the American Society for Clinical Investigation and the National Academy of Medicine.
Genome Informatics is a scientific study of information processing in genomes.
Melissa Anne Haendel is an American bioinformaticist who is the Chief Research Informatics Officer of the Anschutz Medical Campus of the University of Colorado as well as a Professor of Biochemistry and Molecular Genetics and the Marsico Chair in Data Science. She serves as Director of the Center for Data to Health (CD2H). Her research makes use of data to improve the discovery and diagnosis of diseases. During the COVID-19 pandemic, Haendel joined with the National Institutes of Health to launch the National COVID Cohort Collaborative (N3C), which looks to identify the risk factors that can predict severity of disease outcome and help to identify treatments.
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding biological and medical data. It can be viewed as the study and application of data science to solve biomedical problems. Modern biomedical datasets often have specific features which make their analyses difficult, including:
