Chromosomal translocationIn genetics, chromosome translocation is a phenomenon that results in unusual rearrangement of chromosomes. This includes balanced and unbalanced translocation, with two main types: reciprocal, and Robertsonian translocation. Reciprocal translocation is a chromosome abnormality caused by exchange of parts between non-homologous chromosomes. Two detached fragments of two different chromosomes are switched. Robertsonian translocation occurs when two non-homologous chromosomes get attached, meaning that given two healthy pairs of chromosomes, one of each pair "sticks" and blends together homogeneously.
Threading (protein sequence)In molecular biology, protein threading, also known as fold recognition, is a method of protein modeling which is used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure. It differs from the homology modeling method of structure prediction as it (protein threading) is used for proteins which do not have their homologous protein structures deposited in the Protein Data Bank (PDB), whereas homology modeling is used for those proteins which do.
Statistical classificationIn statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.
Protein aggregationIn molecular biology, protein aggregation is a phenomenon in which intrinsically-disordered or mis-folded proteins aggregate (i.e., accumulate and clump together) either intra- or extracellularly. Protein aggregates have been implicated in a wide variety of diseases known as amyloidoses, including ALS, Alzheimer's, Parkinson's and prion disease. After synthesis, proteins typically fold into a particular three-dimensional conformation that is the most thermodynamically favorable: their native state.
Learning classifier systemLearning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling, classification, data mining, regression, function approximation, or game strategy).
Polymerase chain reactionThe polymerase chain reaction (PCR) is a method widely used to make millions to billions of copies of a specific DNA sample rapidly, allowing scientists to amplify a very small sample of DNA (or a part of it) sufficiently to enable detailed study. PCR was invented in 1983 by American biochemist Kary Mullis at Cetus Corporation; Mullis and biochemist Michael Smith, who had developed other essential ways of manipulating DNA, were jointly awarded the Nobel Prize in Chemistry in 1993.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
In-gel digestionThe in-gel digestion step is a part of the sample preparation for the mass spectrometric identification of proteins in course of proteomic analysis. The method was introduced in 1992 by Rosenfeld. Innumerable modifications and improvements in the basic elements of the procedure remain. The in-gel digestion step primarily comprises the four steps; destaining, reduction and alkylation (R&A) of the cysteines in the protein, proteolytic cleavage of the protein and extraction of the generated peptides.
Forensic identificationForensic identification is the application of forensic science, or "forensics", and technology to identify specific objects from the trace evidence they leave, often at a crime scene or the scene of an accident. Forensic means "for the courts". People can be identified by their fingerprints. This assertion is supported by the philosophy of friction ridge identification, which states that friction ridge identification is established through the agreement of friction ridge formations, in sequence, having sufficient uniqueness to individualize.
Protein designProtein design is the rational design of new protein molecules to design novel activity, behavior, or purpose, and to advance basic understanding of protein function. Proteins can be designed from scratch (de novo design) or by making calculated variants of a known protein structure and its sequence (termed protein redesign). Rational protein design approaches make protein-sequence predictions that will fold to specific structures.
Bacterial conjugationBacterial conjugation is the transfer of genetic material between bacterial cells by direct cell-to-cell contact or by a bridge-like connection between two cells. This takes place through a pilus. It is a parasexual mode of reproduction in bacteria. It is a mechanism of horizontal gene transfer as are transformation and transduction although these two other mechanisms do not involve cell-to-cell contact. Classical E. coli bacterial conjugation is often regarded as the bacterial equivalent of sexual reproduction or mating since it involves the exchange of genetic material.
Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
