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Bioinformatics
We are on the cusp of an explosion of biological data generated by the human genome project and sequencing projects in multiple organisms, coupled with advances in both experimental and information technologies. All this is contributing to a new era of personalized medicine, using a deeper understanding of our bodies and their diseases at the molecular level. The huge demand to manage, analyze, and interpret these various data has led to the growing stature of the field of information science that is called bioinformatics.
Bioinformatics encompasses all aspects of biological information—acquisition, processing, storage, distribution, analysis, and interpretation—and combines the tools and techniques of mathematics, computer science, and biology with the aim of furthering the understanding of diseases. The National Institutes of Health defines bioinformatics as “research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.”
Bioinformatics can be viewed as a bottom-up approach, working with molecular data to determine physiological information. In contrast, medical informatics can be viewed as a top-down approach, working with patient clinical data to determine underlying physiological processes. Together, bioinformatics and medical informatics are key methods shaping the future of personalized medicine. This means that, due to bioinformatics analysis of genomic data, medical decision making is evolving to be based on a person's individual genomic information instead of on studies relying on statistics about the general population.
History
As a field of biological and information science, bioinformatics has been present since the discovery of DNA, when proteins and cell forms became known as the building blocks of life. The cardinal functions of bioinformatics have been (a) handling and presentation of nucleotide and protein sequences and their annotation; (b) development of databases to store, analyze, and interpret these data; and (c) development of algorithms for making predictions based on available information. To address these topics, the field drew from the foundations of statistics, mathematics, physics, computer science, and molecular biology. Bioinformatics still reflects this broad base.
The Human Genome
The genome's language is a DNA code containing an alphabet of just four letters, or bases: G, C, A, and T. Remarkably, the entire human genome contains 3 billion of these DNA bases. While sequencing the human genome to decode these billions of bases in multiple people from different ethnicities, bioinformatics technologies were used and improved to view, combine, compare, and find patterns across this enormous amount of data. By comparing sequences of known and unknown genes, bioinformatics programs were developed that used probabilities and modeling to predict the function and roles of previously unknown genes. This vast amount of completed genomic sequence data and individual gene information now also needed to be stored, bringing about the creation of various gene databases that are publicly available.
During this genomic era, bioinformatics tools played a pivotal role in allowing researchers to generate and compare the DNA sequences of many genes to identify their roles and to determine whether a particular gene sequence has different DNA bases than seen normally. This information has provided insights into many biochemical, evolutionary, and genetic pathways. It has also provided an important building block for potential medical decision making by making it possible to identify whether a patient's specific gene is normal or mutated.
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- Basis for Making the Decision
- Acceptability Curves and Confidence Ellipses
- Beneficence
- Bioethics
- Choice Theories
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- Judgment
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- Learning and Memory in Medical Training
- Lens Model
- Lottery
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- Memory Reconstruction
- Mental Accounting
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- Pain
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- Preference Reversals
- Probability Errors
- Probability, Verbal Expressions of
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- Prospect Theory
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- Risk Perception
- Scaling
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- Social Judgment Theory
- Stigma Susceptibility
- Support Theory
- Uncertainty in Medical Decisions
- Unreliability of Memory
- Value Functions in Domains of Gains and Losses
- Worldviews
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