|
Epiphenotyping
Epiphenotyping involves studying the relationship between DNA methylation patterns and phenotypic traits in individuals and populations to be able to predict a phenotype from a DNA methylation profile. In the following sections, the background of epiphenotyping, an overview of a general methodology, its applications, advantages, and limitations are covered. Epigenetics refers to heritable changes in gene expression that are not changes in the underlying DNA sequence... DNA methylation is a key epigenetic mechanism, involving the addition of methyl (-CH3) groups to specific DNA regions, almost always at cytosine-guanine dinucleotides (CpG sites). CpG sites are DNA sequences where a cytosine nucleotide is followed by a guanine nucleotide connected by a phosphate group. Background The term ''epiphenotyping'' comes from integrating the two words "epigenetics" and "phenotyping". Epiphenotyping is the process of using genome-wide DNA methylation patterns to predict phenotypes. Throu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Epigenetics
In biology, epigenetics is the study of changes in gene expression that happen without changes to the DNA sequence. The Greek prefix ''epi-'' (ἐπι- "over, outside of, around") in ''epigenetics'' implies features that are "on top of" or "in addition to" the traditional (DNA sequence based) genetic mechanism of inheritance. Epigenetics usually involves a change that is not erased by cell division, and affects the regulation of gene expression. Such effects on cellular and physiological traits may result from environmental factors, or be part of normal development. The term also refers to the mechanism of changes: functionally relevant alterations to the genome that do not involve mutation of the nucleotide sequence. Examples of mechanisms that produce such changes are DNA methylation and histone modification, each of which alters how genes are expressed without altering the underlying DNA sequence. Further, non-coding RNA sequences have been shown to play a key role in the r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phenotypic Trait
A phenotypic trait, simply trait, or character state is a distinct variant of a phenotypic characteristic of an organism; it may be either inherited or determined environmentally, but typically occurs as a combination of the two.Lawrence, Eleanor (2005) ''Henderson's Dictionary of Biology''. Pearson, Prentice Hall. For example, having eye color is a ''character'' of an organism, while blue, brown and hazel versions of eye color are ''traits''. The term ''trait'' is generally used in genetics, often to describe the phenotypic expression of different combinations of alleles in different individual organisms within a single population, such as the famous purple vs. white flower coloration in Gregor Mendel's pea plants. By contrast, in systematics, the term ''character state'' is employed to describe features that represent fixed diagnostic differences among taxa, such as the absence of tails in great apes, relative to other primate groups. Definition A phenotypic trait is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DNA Methylation
DNA methylation is a biological process by which methyl groups are added to the DNA molecule. Methylation can change the activity of a DNA segment without changing the sequence. When located in a gene promoter (genetics), promoter, DNA methylation typically acts to repress gene Transcription (genetics), transcription. In mammals, DNA methylation is essential for normal development and is associated with a number of key processes including genomic imprinting, X-chromosome inactivation, repression of transposable elements, aging, and carcinogenesis. As of 2016, two nucleobases have been found on which natural, enzymatic DNA methylation takes place: adenine and cytosine. The modified bases are N6-methyladenine,D. B. Dunn, J. D. Smith: "The occurrence of 6-methylaminopurine in deoxyribonucleic acids". In: ''Biochem J.'' 68(4), Apr 1958, S. 627–636. [//www.ncbi.nlm.nih.gov/pubmed/13522672?dopt=Abstract PMID 13522672]. . 5-methylcytosineB. F. Vanyushin, S. G. Tkacheva, A. N. Belozers ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Whole Genome Bisulfite Sequencing
Whole genome bisulfite sequencing is a next-generation sequencing technology used to determine the DNA methylation status of single cytosines by treating the DNA with sodium bisulfite before high-throughput DNA sequencing. The DNA methylation status at various genes can reveal information regarding gene regulation and transcriptional activities. This technique was developed in 2009 along with reduced representation bisulfite sequencing after bisulfite sequencing became the gold standard for DNA methylation analysis. Whole genome bisulfite sequencing measures single-cytosine methylation levels genome-wide and directly estimates the ratio of molecules methylated rather than enrichment levels. Currently, this technique has recognized and tested approximately 95% of all cytosines in known genomes. With the improvement of library preparation methods and next-generation sequencing technology over the past decade, whole genome bisulfite sequencing has become an increasingly widespread a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Normalization (statistics)
In statistics and applications of statistics, normalization can have a range of meanings. In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment. In the case of normalization of scores in educational assessment, there may be an intention to align distributions to a normal distribution. A different approach to normalization of probability distributions is quantile normalization, where the quantiles of the different measures are brought into alignment. In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal Component Analysis
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where the i-th vector is the direction of a line that best fits the data while being orthogonal to the first i-1 vectors. Here, a best-fitting line is defined as one that minimizes the average squared perpendicular distance from the points to the line. These directions (i.e., principal components) constitute an orthonormal basis in which different individual dimensions of the data are linearly uncorrelated. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Random Forest
Random forests or random decision forests is an ensemble learning method for statistical classification, classification, regression analysis, regression and other tasks that works by creating a multitude of decision tree learning, decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of the trees. Random forests correct for decision trees' habit of overfitting to their Test set, training set. The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg. An extension of the algorithm was developed by Leo Breiman and Adele Cutler, who registered "Random Forests" as a trademark in 2006 (, owned by Minitab, Minitab, Inc.). The extension combines Breiman's ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Support Vector Machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). In addition to performing linear classification, SVMs can efficiently perform non-linear classification using the ''kernel trick'', representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function, which transforms them into coordinates in a higher-dimensional feature space. Thus, SVMs use the kernel trick to implicitly map their inputs into high-dimensional feature spaces, where linear classification can be performed. Being max-margin models, SVMs are resilient to noisy data (e.g., misclassified examples). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Examples Of Epiphenotyping Applications
Example may refer to: * ''exempli gratia'' (e.g.), usually read out in English as "for example" * .example, reserved as a domain name that may not be installed as a top-level domain of the Internet ** example.com, example.net, example.org, and example.edu: second-level domain names reserved for use in documentation as examples * HMS ''Example'' (P165), an Archer-class patrol and training vessel of the Royal Navy Arts * ''The Example'', a 1634 play by James Shirley * ''The Example'' (comics), a 2009 graphic novel by Tom Taylor and Colin Wilson * Example (musician), the British dance musician Elliot John Gleave (born 1982) * ''Example'' (album), a 1995 album by American rock band For Squirrels See also * Exemplar (other), a prototype or model which others can use to understand a topic better * Exemplum, medieval collections of short stories to be told in sermons * Eixample The Eixample (, ) is a district of Barcelona between the old city (Ciutat Vella) a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
