Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Apriori Algorithm <s> Autism spectrum disorders (ASD) represent a group of developmental disorders with strong genetic underpinnings. To explore the genetic complexity of ASD, we developed AutMiner (), a public web-portal for the collection of genes linked to ASD, and the implementation of an autism-centre network. AutMiner extracts candidate genes associated with ASD using text mining from 9276 abstracts. Compared to other recent systems, gene entries are richer to provide a reference for clinical geneticists. AutMiner also constructs ASD-related network consisting of autism-gene network and gene-gene network. To the best of our knowledge, this is the first web example of ASD-related network. The major focus of AutMiner is to offer a valuable reference tool for clinical geneticists in establishing and implementing effective genetic screening programmes for those patients with ASD. <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Apriori Algorithm <s> Autism is a complex neuropsychiatric disorder with high heritability and an unclear etiology. The identification of key genes related to autism may elucidate its etiology. The current study provides an approach to predicting autism susceptibility genes. Genes are first extracted from the biomedical literature, and some autism susceptibility genes are then recognized as seeds by the prior knowledge. As candidates, the remaining genes are predicted by creating association rules between the seeds and candidates. In an evaluated data set, 27 autism susceptibility genes (type “Y”) are extracted and 43 possible autism susceptibility genes (type “P”) are predicted. The sum of “Y” and “P” genes accounts for 93.3% of the data set that are not contained in the typical database of autism susceptibility genes. Our approach can effectively extract and predict autism susceptibility genes from the biomedical literature. These predicted results complement the typical database of autism susceptibility genes. The web portal for the predicted results, which is freely available at http://biolab.hyit.edu.cn/ar, can be a valuable resource in studies of diseases related to genes. © 2012 Wiley Peridicals, Inc. <s> BIB002
Apriori algorithms look to find the k most common terms across a dataset. At each pass over the data, the algorithm adds to its prior terms other terms that appear frequently alongside the term sets it has collected previously. The Apriori algorithm is useful because it is straightforward and can be used on massive datasets relatively quickly. It generally relies on the assumption that a set cannot be frequent unless its subsets are frequent. These frequent sets are used to create association rules that expose trends in the dataset  . It has practical uses in areas like advertising and retail sales because it is able to group items that are viewed or purchased together frequently. Like FSMTN, this algorithm has been used in the reviewed ASD literature to investigate ASD candidate genes. BIB002 extended the work of BIB001 using association rules. The authors extracted genes from 16,869 PubMed biomedical journal abstracts related to ASD. Based on prior knowledge, the identified genes were classified as either seed (i.e., recognized ASD susceptibility genes) or candidate. To predict new ASD susceptibility genes, association rules were created between the identified seed and candidate genes. Motivated by the Apriori algorithm, the authors developed the KF algorithm to find key factors of the candidate genes. The KF algorithm used a seed gene list, candidate gene list, and document-gene table as inputs. A total of 27 ASD susceptibility genes were found, and 43 possible susceptibility genes were predicted. These findings further underline the promising use of text mining to identify new genes potentially associated with ASD.