Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Random Walk <s> The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for efficient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information. <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Random Walk <s> Despite the known existence of distant-acting cis-regulatory elements in the human genome, only a small fraction of these elements has been identified and experimentally characterized in vivo. This paucity of enhancer collections with defined activities has thus hindered computational approaches for the genome-wide prediction of enhancers and their functions. To fill this void, we utilize comparative genome analysis to identify candidate enhancer elements in the human genome coupled with the experimental determination of their in vivo enhancer activity in transgenic mice (1). These data are available through the VISTA Enhancer Browser (http://enhancer.lbl.gov). This growing database currently contains over 250 experimentally tested DNA fragments, of which more than 100 have been validated as tissue-specific enhancers. For each positive enhancer, we provide digital images of whole-mount embryo staining at embryonic day 11.5 and an anatomical description of the reporter gene expression pattern. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to a particular tissue, or download entire collections of enhancers with a defined tissue specificity or conservation depth. These experimentally validated training sets are expected to provide a basis for a wide range of downstream computational and functional studies of enhancer function. <s> BIB002 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Random Walk <s> We have developed Lynx (http://lynx.ci.uchicago. edu)—a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and networkbased gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces. <s> BIB003
Random walk is a way to explore a space using a series of probabilistically determined steps. The probability that the next step will be in a certain direction can be predetermined. Random walks are used to calculate both expected distance after n moves as well as the probability of landing in certain regions after n steps  . Random walks have been used to simulate complex behavior in many applications from sports analytics to stock prices. In the reviewed text mining literature, random walk has been used in the development of a biomedical database used to promote knowledge discovery across many disorders of interest. Ontologies BIB003 created Lynx, an online database and knowledge extraction tool to aid in the analysis of biomedical data. Lynx consists of an integrated knowledgebase, LynxKB, and knowledge extraction services, including the use of the text mining system GeneWays BIB001 ) and the Vista project BIB002 ). Gene prioritization was accomplished with five propagation algorithms: simple random walk, heat kernel diffusion, PageRank with priors, HITS with priors, and K-step Markov. The authors conducted a case study using Lynx to investigate the genes and molecular mechanisms associated with the occurrence of seizures in ASD. A total of 59 genes were found to be associated with ASD and seizures. After ranking the identified genes, 31 were found to be potentially related to the symptom of seizures in ASD. These findings demonstrate how text mining can be harnessed to extract knowledge from vast collections of biomedical data.