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An ontology is a framework for describing domain-specific knowledge in a structured format. It is comprised of a set of terms as nodes and a set of relationships between terms as directed edges to form a directed acyclic graph. Gene Ontology (GO) and Human Phenotype Ontology (HPO) are widely referred biological and biomedical ontology databases. They also provide extensive annotations of human genes...
The recent advance in SNP genotyping has made a significant contribution to reduction of the costs for large-scale genotyping. The development also has dramatically increased the size of the SNP genotype data. The increase of the volume of the data, however, has posed a huge obstacle to the conventional analysis techniques that are typically vulnerable to the high-dimensionality problem. To address...
Genome-wide association studies (GWAS) have served crucial roles in investigating disease susceptible loci for single traits. On the other hand, the GWAS have been limited in measuring genetic risk factors for multivariate phenotypes from pleiotropic genetic effects of genetic loci. This work reports a data mining approach to discover patterns of multivariate phenotypes expressed as association rules,...
Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet...
Zhongjing prescriptions are selected as data set in this paper. First, a classification association rules mining algorithm, which selected features based on information gain, short for CARM-IG, is achieved. The algorithm is used to mine compatibility laws of Zhang Zhongjing from cold-heat, deficiency-excess in eight principals of TCM (Traditional Chinese Medicine). Second, 21 rules which are applied...
Identifying possible viral-host protein-protein interactions is an important and useful approach in developing new drugs targeting those interactions. In this article, a recently published dataset containing records of interactions between a set of HIV-1 proteins and a set of human proteins has been analyzed using association rule mining. The main objective is to identify a set of association rules...
Environment, customs and health status in northwest minority areas have been studied. We found the critical factors to prevent cerebral infraction. First rough sets theory had been used to reduce the attributes, secondly association rules had been used, finally logistic regression model had been used. The model solved the shortcomings of too many rules that caused by attribute redundancy and reliability...
Extracting association rules from data with both discrete and continuous attributes is an important problem in KDD. A new model of immune genetic algorithm is formulated for solving this problem. This algorithm uses three-segment chromosomes, integrating the discretization, attributes reduction and mining association rules. And immune mechanism is introduced into genetic algorithm to avoid premature...
To dynamic increasing databases, the data dynamic reduction and decision-making rule mining are treated by the methods of repeat scan, order, search, reduction data set traditionally, this paper proposes a new mining algorithm, which treat two dispart table simultaneously by using program' many course parallel technology. This method improves greatly mining efficiency of the system, is of important...
Concept lattice represents knowledge with the relationships between the intent and the extent of concepts, and the relations between the generalization and the specialization of concepts, then knowledge can be shown on the Hasse diagram with hierarchical structure, thus it is properly applied to the description of mining association rules in databases. Compared with well-known Apriori and FP-Growth...
Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression...
One of the most demanding problems in mining temporal data is to identify how multivariate change associations might be discovered and used to better understand data interactions and dependencies. This paper introduces a framework to mine associations among significant changes in multivariate time-series data. Building on statistical methods, we detect significant changes in time-series data and use...
Mining frequent closed patterns play an important role in mining association rules in microarray data. The bottom-up search strategy for mining frequent closed patterns cannot make full use of minimum support threshold to prune search space and results in long runtime and much memory overhead. TP+close algorithm based on top-down search strategy addressed the problem. However, it determined a frequent...
Temporal data mining is becoming an important tool for health care providers and decision makers. The capability of handling and analyzing complex multivariate data may allow to extract useful information coming from the day-by-day activity of health care organizations as well as from patients monitoring. In this paper we review the main approaches presented in the literature to mine biomedical time...
This study's objective is to solve discovering knowledge issue from data of brain function image for recognition of Chinese words. The concern is formulated as data mining. In particular, this paper presented an association rules data-mining model based on software architecture. The model is characterized by transparent conversions between heterogeneous data formats using MDI (metadata information),...
Breast cancer is the second most common cancer worldwide and the fifth most common cause of cancer death. There are many prognostic factors associated with breast cancer which are usually considered when determining how cancer will affect a patient. In addition, distinct molecular subtypes of breast tumors have been described by gene expression profiling. In this work we integrate information from...
The increase in autism prevalence has been the motivation for much research which has produced various theories for its causation. Genetic and environmental factors have been investigated. An area of focus is the affect of exposure to neurotoxins, such as mercury and lead, during critical stages in a childpsilas early development. In this study we apply Combinatorial Fusion Analysis (CFA) and Association...
Protein sequence motifs information is crucial to the analysis of biologically significant regions. The conserved regions have the potential to determine the role of the proteins. Many algorithms or techniques to discover motifs require a predefined fixed window size in advance. Due to the fixed size, these approaches often deliver a number of similar motifs simply shifted by some bases or including...
We investigate the problem of predicting protein-protein interaction (PPI) using numerical features constructed from parent-child relation of a partial network constructed from known protein interactions. For each pair of proteins, we use a validation-based approach to normalize these features, which are based on association rule interestingness measures. The primary contribution of this work is the...
Many algorithms have been proposed to solve the problem of mining frequent itemset. The resulting frequent itemsets represent the global frequent patterns. This global output doesn't provide any information about the distribution of the frequent patterns on the database. This missing information can produce inaccurate decisions or prediction when the output frequent itemsets are used in decision support...
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