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      Finding Top- k Covering Irreducible Contrast Sequence Rules for Disease Diagnosis

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          Abstract

          Diagnostic genes are usually used to distinguish different disease phenotypes. Most existing methods for diagnostic genes finding are based on either the individual or combinatorial discriminative power of gene(s). However, they both ignore the common expression trends among genes. In this paper, we devise a novel sequence rule, namely, top- k irreducible covering contrast sequence rules (Top kIRs for short), which helps to build a sample classifier of high accuracy. Furthermore, we propose an algorithm called MineTop kIRs to efficiently discover Top kIRs. Extensive experiments conducted on synthetic and real datasets show that MineTop kIRs is significantly faster than the previous methods and is of a higher classification accuracy. Additionally, many diagnostic genes discovered provide a new insight into disease diagnosis.

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          Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

          T. Golub (1999)
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            Gene-expression profiles in hereditary breast cancer.

            I Hedenfalk, D. Duggan, Y. Chen … (2001)
            Many cases of hereditary breast cancer are due to mutations in either the BRCA1 or the BRCA2 gene. The histopathological changes in these cancers are often characteristic of the mutant gene. We hypothesized that the genes expressed by these two types of tumors are also distinctive, perhaps allowing us to identify cases of hereditary breast cancer on the basis of gene-expression profiles. RNA from samples of primary tumor from seven carriers of the BRCA1 mutation, seven carriers of the BRCA2 mutation, and seven patients with sporadic cases of breast cancer was compared with a microarray of 6512 complementary DNA clones of 5361 genes. Statistical analyses were used to identify a set of genes that could distinguish the BRCA1 genotype from the BRCA2 genotype. Permutation analysis of multivariate classification functions established that the gene-expression profiles of tumors with BRCA1 mutations, tumors with BRCA2 mutations, and sporadic tumors differed significantly from each other. An analysis of variance between the levels of gene expression and the genotype of the samples identified 176 genes that were differentially expressed in tumors with BRCA1 mutations and tumors with BRCA2 mutations. Given the known properties of some of the genes in this panel, our findings indicate that there are functional differences between breast tumors with BRCA1 mutations and those with BRCA2 mutations. Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancers with BRCA2 mutations. Our results suggest that a heritable mutation influences the gene-expression profile of the cancer.
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              Biomarker identification by feature wrappers.

              J. Zhao, X. W. Fang, M Xiong (2001)
              Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being "wrapped around": linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Math Methods Med
                Journal ID (iso-abbrev): Comput Math Methods Med
                Journal ID (publisher-id): CMMM
                Title: Computational and Mathematical Methods in Medicine
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1748-670X
                ISSN (Electronic): 1748-6718
                Publication date (Print): 2015
                Publication date (Electronic): 10 March 2015
                Volume: 2015
                Electronic Location Identifier: 353146
                Affiliations
                1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
                2Software Center, Northeastern University, Shenyang, Liaoning 110004, China
                Author notes

                Academic Editor: Lev Klebanov

                Author information
                http://orcid.org/0000-0002-7995-8590
                http://orcid.org/0000-0001-7089-1302
                Article
                DOI: 10.1155/2015/353146
                PMC ID: 4381988
                SO-VID: 465faf08-0dbf-4efd-b5bd-1823e27ff639
                Copyright © Copyright © 2015 Yuhai Zhao et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 1 October 2014
                Date accepted : 20 January 2015
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Applied mathematics
                Data availability:
                ScienceOpen disciplines: Applied mathematics

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