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Power Graph Analysis
In computational biology, power graph analysis is a method for the analysis and representation of complex networks. Power graph analysis is the computation, analysis and visual representation of a power graph from a graph (networks). Power graph analysis can be thought of as a lossless compression algorithm for graphs. It extends graph syntax with representations of cliques, bicliques and stars. Compression levels of up to 95% have been obtained for complex biological networks. Hypergraphs are a generalization of graphs in which edges are not just couples of nodes but arbitrary n-tuples. Power graphs are not another generalization of graphs, but instead a novel representation of graphs that proposes a shift from the "node and edge" language to one using cliques, bicliques and stars as primitives. Power graphs Graphical representation Graphs are drawn with circles or points that represent nodes and lines connecting pairs of nodes that represent edges. Power graphs extend the sy ...
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Computational Biology
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. History Bioinformatics, the analysis of informatics processes in biological systems, began in the early 1970s. At this time, research in artificial intelligence was using network models of the human brain in order to generate new algorithms. This use of biological data pushed biological researchers to use computers to evaluate and compare large data sets in their own field. By 1982, researchers shared information via punch cards. The amount of data grew exponentially by the end of the 1980s, requiring new computational methods for quickly interpreting ...
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Surjective Function
In mathematics, a surjective function (also known as surjection, or onto function) is a function that every element can be mapped from element so that . In other words, every element of the function's codomain is the image of one element of its domain. It is not required that be unique; the function may map one or more elements of to the same element of . The term ''surjective'' and the related terms '' injective'' and ''bijective'' were introduced by Nicolas Bourbaki, a group of mainly French 20th-century mathematicians who, under this pseudonym, wrote a series of books presenting an exposition of modern advanced mathematics, beginning in 1935. The French word ''sur'' means ''over'' or ''above'', and relates to the fact that the image of the domain of a surjective function completely covers the function's codomain. Any function induces a surjection by restricting its codomain to the image of its domain. Every surjective function has a right inverse assuming the axi ...
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Complex Networks
Complex Networks is an American media and entertainment company for youth culture, based in New York City. It was founded as a bi-monthly magazine, ''Complex'', by fashion designer Marc (Ecko) Milecofsky. Complex Networks reports on popular and emerging trends in style, sneakers, food, music, sports and pop culture. Complex Networks reached over 90 million unique users per month in 2013 across its owned and operated and partner sites, socials and YouTube channels. The print magazine ceased publication with the December 2016/January 2017 issue. Complex currently has 4.55 million subscribers and 1.3 billion total views on YouTube. As of 2019, the company's yearly revenue was estimated to be US$200 million, 15% of which came from commerce. Complex Networks has been named by ''Business Insider'' as one of the Most Valuable Startups in New York, and Most Valuable Private Companies in the World. Complex Networks CEO Rich Antoniello was named among the Silicon Alley 100. In 2012, t ...
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Computational Biology
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. History Bioinformatics, the analysis of informatics processes in biological systems, began in the early 1970s. At this time, research in artificial intelligence was using network models of the human brain in order to generate new algorithms. This use of biological data pushed biological researchers to use computers to evaluate and compare large data sets in their own field. By 1982, researchers shared information via punch cards. The amount of data grew exponentially by the end of the 1980s, requiring new computational methods for quickly interpreting ...
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Drug Repositioning
Drug repositioning (also called drug repurposing) involves the investigation of existing drugs for new therapeutic purposes. Repurposing achievements A number of successes have been achieved, the foremost including sildenafil (Viagra) for erectile dysfunction and pulmonary hypertension and thalidomide for leprosy and multiple myeloma. Clinical trials have been performed on posaconazole and ravuconazole for Chagas disease. Other antifungal agents clotrimazole and ketoconazole have been investigated for anti- trypanosome therapy. Successful repositioning of antimicrobials has led to the discovery of broad-spectrum therapeutics, which are effective against multiple infection types. In psychiatry, repurposed drugs are emerging as feasible options to treat severe mental disorders. Strategy Drug repositioning is a "universal strategy" for neglected diseases due to 1) reduced number of required clinical trial steps could reduce the time and costs for the medicine to reach mark ...
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Gene Regulatory Networks
A gene (or genetic) regulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo). The regulator can be DNA, RNA, protein or any combination of two or more of these three that form a complex, such as a specific sequence of DNA and a transcription factor to activate that sequence. The interaction can be direct or indirect (through transcribed RNA or translated protein). In general, each mRNA molecule goes on to make a specific protein (or set of proteins). In some cases this protein will be structural, and will accumulate at the cell membrane or within the cell to give it particular structural properties. In other cases the protein will be an enzyme, ...
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Complex Networks
Complex Networks is an American media and entertainment company for youth culture, based in New York City. It was founded as a bi-monthly magazine, ''Complex'', by fashion designer Marc (Ecko) Milecofsky. Complex Networks reports on popular and emerging trends in style, sneakers, food, music, sports and pop culture. Complex Networks reached over 90 million unique users per month in 2013 across its owned and operated and partner sites, socials and YouTube channels. The print magazine ceased publication with the December 2016/January 2017 issue. Complex currently has 4.55 million subscribers and 1.3 billion total views on YouTube. As of 2019, the company's yearly revenue was estimated to be US$200 million, 15% of which came from commerce. Complex Networks has been named by ''Business Insider'' as one of the Most Valuable Startups in New York, and Most Valuable Private Companies in the World. Complex Networks CEO Rich Antoniello was named among the Silicon Alley 100. In 2012, t ...
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Modular Decomposition
In graph theory, the modular decomposition is a decomposition of a graph into subsets of vertices called modules. A ''module'' is a generalization of a connected component of a graph. Unlike connected components, however, one module can be a proper subset of another. Modules therefore lead to a recursive (hierarchical) decomposition of the graph, instead of just a partition. There are variants of modular decomposition for undirected graphs and directed graphs. For each undirected graph, this decomposition is unique. This notion can be generalized to other structures (for example directed graphs) and is useful to design efficient algorithms for the recognition of some graph classes, for finding transitive orientations of comparability graphs, for optimization problems on graphs, and for graph drawing. Modules As the notion of modules has been rediscovered in many areas, ''modules'' have also been called ''autonomous sets'', ''homogeneous sets'', ''stable sets'', ''cl ...
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Jaccard Index
The Jaccard index, also known as the Jaccard similarity coefficient, is a statistic used for gauging the similarity and diversity of sample sets. It was developed by Grove Karl Gilbert in 1884 as his ratio of verification (v) and now is frequently referred to as the Critical Success Index in meteorology. It was later developed independently by Paul Jaccard, originally giving the French name ''coefficient de communauté'', and independently formulated again by T. Tanimoto. Thus, the Tanimoto index or Tanimoto coefficient are also used in some fields. However, they are identical in generally taking the ratio of Intersection over Union. The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets: : J(A,B) = = . Note that by design, 0\le J(A,B)\le 1. If ''A'' intersection ''B'' is empty, then ''J''(''A'',''B'') = 0. The Jaccard coefficient is widely used ...
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Hierarchical Clustering
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: * Agglomerative: This is a " bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. * Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of \mathcal(n^3) and requires \Omega(n^2) memory, which makes it too slow for even medium data sets. However, for some special cases, optimal efficient agglomerative methods (of comp ...
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