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Integrating Gene Expression and Phenotypic Information to Analyze Alzheimer's Disease

JOURNAL OF ALZHEIMERS DISEASE 2009年第1期16卷 73-84页

作者： Ray, Monika Zhang, Weixiong Washington Univ Dept Comp Sci & Engn St Louis MO USA Washington Univ Sch Med Dept Genet St Louis MO 63110 USA

The assessment of the relationship between gene expression profiling, clinical and histopathological phenotypes would be better suited to understanding Alzheimer's disease (AD) pathogenesis. We developed a multiple linear regression (MLR) method to simultaneously model three variables - Mini-Mental Status Examination (MMSE) score, neurofibrillary tangles (NFT) score and gene expression profile - to identify significant genes. These genes were also used to distinguish subjects with incipient AD from healthy controls. Finally we investigated the behavior of the significant genes across the entorhinal cortex and hippocampus of AD subjects in two different Braak stages. Results indicate that integrating multiple phenotypic and gene expression information of samples increases the power of methods while analyzing small datasets. The MLR method could identify significant genes at reasonable false discovery rates (FDRs), thereby providing a choice of reasonable FDRs. The accuracy in discriminating between subjects affected and unaffected by AD using MLR identified genes was high. We found that transcription and tumor suppressor responses do begin quite early in AD and therefore should be the target of drugs. Several genes were consistently up/down-regulated across the two brain regions and Braak stages and, therefore, can be used as predictive markers to detect AD at an earlier stage.

关键词： Alzheimer's disease classification gene selection microarray data Mini-Mental Status Examination neurofibrillary tangles score

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Extending the pathway analysis framework with a test for transcriptional variance implicates novel pathway modulation during myogenic differentiation

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BIOINFORMATICS 2007年第11期23卷 1356-1362页

作者： Kemp, Daniel M. Nirmala, N. R. Szustakowski, Joseph D. Novartis Inst Biomed Res Diabet & Metab Dis Area Cambridge MA 02139 USA Novartis Inst Biomed Res Cambridge MA 02139 USA

Motivation: We describe an extension of the pathway-based enrichment approach for analyzing microarray data via a robust test for transcriptional variance. The use of a variance test is intended to identify additional patterns of transcriptional regulation in which many genes in a pathway are up- and down-regulated. Such patterns may be indicative of the reciprocal regulation of pathway activators and inhibitors or of the differential regulation of separate biological sub-processes and should extend the number of detectable patterns of transcriptional modulation. Results: We validated this new statistical approach on a microarray experiment that captures the temporal transcriptional profile of muscle differentiation in mouse C2C12 cells. Comparisons of the transcriptional state of myoblasts and differentiated myotubes via a robust variance test implicated several novel pathways in muscle cell differentiation previously overlooked by a standard enrichment analysis. Specifically, pathways involved in cell structure, calcium-mediated signaling and muscle-specific signaling were identified as differentially modulated based on their increased transcriptional variance. These biologically relevant results validate this approach and demonstrate the flexible nature of pathway-based methods of data analysis.

关键词： .Differentially robust down-regulated microarray data reciprocal myogenic myoblasts myotubes pathway-based transcriptional variance pathway analysis framework variance test genes in pathway pathway activators standard enrichment

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Identifying the candidate genes using co-expression, GO, and machine learning techniques for Alzheimer's disease

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022年第1期11卷 1-12页

作者： Sahu, Shailendra Dholaniya, Pankaj Singh Rani, T. Sobha Univ Hyderabad Sch Comp & Informat Sci Hyderabad India Univ Hyderabad Dept Biotechnol & Bioinformat Hyderabad India

Alzheimer's disease is a neurological disorder that affects an individual's memory, motor functions, behaviour, and thought process. It has been observed that the hippocampus is the first region that gets affected by Alzheimer's. Hence, a study of the hippocampus region can identify genes responsible for the occurrence of the early stage of the disease. Most often, t-test and correlation are used to identify significant genes at the initial level. As the genes are differentially expressed, their classification power is generally high. These genes might appear significant, but their degree of specificity towards the disease might be low, leading to misleading interpretations. Similarly, there may be many false correlations between the genes that can affect the identification of relevant genes. This paper introduces a new framework to reduce the false correlations and find the potential biomarkers for the disease. The framework concerned uses the t-test, correlation, Gene Ontology (GO) categories, and machine learning techniques to find potential genes. The proposed framework detects Alzheimer-related genes and achieves more than 95% classification accuracy in every dataset considered. Some of the identified genes which are directly involved in Alzheimer are APP, GRIN2B, and APLP2. The proposed framework also identifies genes like ZNF621, RTF1, DCH1, and ERBB4, which may play an important role in Alzheimer's. Gene set enrichment analysis (GSEA) is also carried out to determine the major GO categories: down-regulated and up-regulated.

关键词： microarray data Gene co-expression network Gene ontology similarity Feature selection Classification

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Consistent across-tissue signatures of differential gene expression in Crohn's disease

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IMMUNOGENETICS 2005年第10期57卷 709-716页

作者： Hughes, AL Univ S Carolina Dept Biol Sci Columbia SC 29208 USA

An approach based on analysis of variance was applied to raw expression data on 44,760 transcripts in order to identify those with significant differential expression across ileum and colon in Crohn's disease (CD) and ulcerative colitis (UC). The design treated tissue as a block effect, thereby removing this effect statistically and increasing the power to test for effects of disease states (control, CD, and UC). A significant F-statistic for the disease effect was not correlated with the ratios CD/control or UC/control, evidently because many transcripts with high-expression ratios to the control showed inconsistent patterns across tissues. Of 1,053 transcripts showing a significant effect of disease state at the 1% level by the bootstrap test, 508 showed significant difference at the 1% level in a post hoc test for difference between the mean scores for CD and control. These included a number of genes relevant to the mechanism of pathogenesis of CD and a number of genes mapping to genomic regions that have previously shown linkage to CD in association studies.

关键词： Crohn's disease gene expression inflammatory bowel disease microarray data

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Semantic Subgroup Discovery Systems and Workflows in the SDM-Toolkit

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COMPUTER JOURNAL 2013年第3期56卷 304-320页

作者： Vavpetic, Anze Lavrac, Nada Jozef Stefan Inst Ljubljana 1000 Slovenia Univ Nova Gorica Nova Gorica Slovenia

This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic subgroup discovery systems SDM-search for enriched gene sets (SEGS) and SDM-Aleph. These systems are made publicly available in the new SDM-Toolkit for semantic data mining. The toolkit is implemented in the Orange4WS data mining platform that supports knowledge discovery workflow construction from local and distributed data mining services. On the basis of the experimental evaluation of semantic subgroup discovery systems on two publicly available biomedical datasets, the paper results in a thorough quantitative and qualitative evaluation of SDM-SEGS and SDM-Aleph and their comparison with SEGS, a system for enriched gene set discovery from microarray data.

关键词： semantic data mining relational data mining inductive logic programming domain knowledge subgroup discovery ontologies microarray data

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microarray expression analysis using seed-based clustering method

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ON THE CONVERGENCE OF BIO-INFORMATION-, ENVIRONMENTAL-, ENERGY-, SPACE- AND NANO-TECHNOLOGIES, PTS 1 AND 2 2005年第I期277-279卷 343-348页

作者： Shin, M Park, SH Elect & Telecommun Res Inst Bioinformat Team Taejon 305350 South Korea

Clustering methods have been often used to find biologically relevant groups of genes or conditions based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this paper we address a novel clustering approach, called seed-based clustering, where seed genes are first systematically chosen by computational analysis of their expression profiles, and then the clusters are generated by using the seed genes as initial values for k-means clustering. The seed-based clustering method has strong mathematical foundations and requires only a few matrix computations for seed extraction. As a result, it provides stability of clustering results by eliminating randomness in the selection of initial values for cluster generation. Our empirical results reported here indicate that the entire, clustering process can be systematically pursued using seedbased clustering, and that its performance is favorable compared to current approaches.

关键词： microarray data gene expression data analysis clustering algorithm seed extraction

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Detection of Prostate Cancer using Ensemble based Bi-directional Long Short Term Memory Network

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第1期17卷 91-98页

作者： Kaulgud, Sanjeev Hulipalled, Vishwanath Patil, Siddanagouda Somanagouda Metipatil, Prabhuraj REVA Univ Dept Comp & Informat Technol Bengaluru Karnataka India Univ Agr Sci Dept Appl Maths & Comp Sci Bengaluru Karnataka India

Aim and Background In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate *** The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene *** The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer *** The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.

关键词： Bi-long short-term memory network machine learning ensemble feature optimization technique metaheuristic optimizers microarray data prostate cancer

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Improved breast cancer prognosis through the combination of clinical and genetic markers

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BIOINFORMATICS 2007年第1期23卷 30-37页

作者： Sun, Yijun Goodison, Steve Li, Jian Liu, Li Farmerie, William Univ Florida Interdisciplinary Ctr Biotechnol Res Gainesville FL 32611 USA Univ Florida Dept Surg Gainesville FL 32611 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Motivation: Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary. Methods: A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches. Results: The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant metastases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI:6.5-68.3), far higher than either genetic or clinical markers alone.

关键词： TERM FOLLOW-UP MOLECULAR CLASSIFICATION microarray data EXPRESSION METASTASIS PROTEIN CONSENSUS SELECTION SURVIVAL THERAPY

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A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2018年第2期10卷 244-250页

作者： Anand, Divya Pandey, Babita Pandey, Devendra K. Lovely Profess Univ Sch Comp Sci & Engn Chaheru Punjab India Lovely Profess Univ Sch Comp Applicat Chaheru Punjab India Lovely Profess Univ Sch Biosci Chaheru Punjab India

An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray technology is employed to monitor the level of activity or expression of large number of genes simultaneously. The gene expression data derived from the microarray experiment usually involve a large number of genes but a very few number of samples. There is a need to reduce the dimension of gene expression data which intends to find a small set of discriminative genes that accurately classifies the samples of various kinds of diseases. So, our goal is to find a small subset of genes which ensures the accurate classification of neuromuscular disorders. In the present paper, we propose a novel hybrid feature selection model for classification of neuromuscular disorders. The process of feature selection is done in two phases by integrating Bhattacharyya coefficient and genetic algorithm (GA). In the first phase, we find Bhattacharyya coefficient to choose a candidate gene subset by removing the most redundant genes. In the second phase, the target gene subset is created by selecting the most discriminative gene subset by applying GA wherein the fitness function is calculated using radial basis function support vector machine (RBF SVM). The proposed hybrid algorithm is applied on two publicly available microarray neuromuscular disorders datasets. The results are compared with two individual techniques of feature selection, namely Bhattacharyya coefficient and GA, and one integrated technique, i.e., Bhattacharyya-GA wherein the fitness function of GA is calculated using four other classifiers, which shows that the proposed integrated method is capable of giving the better classification accuracy.

关键词： Bhattacharyya coefficient Genetic algorithm Support vector machine Neuromuscular disorders microarray data Radial basis function

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Prediction of core cancer genes using multi-task classification framework

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JOURNAL OF THEORETICAL BIOLOGY 2013年 317卷 62-70页

作者： Gao, Shan Xu, Shuo Fang, Yaping Fang, Jianwen Univ Kansas Appl Bioinformat Lab Lawrence KS 66047 USA Inst Sci & Tech Informat China Beijing 100038 Peoples R China

Cancer is deemed as a highly heterogeneous disease specific to cell type and tissue origin. All cancers, however, share a common pathogenesis. Therefore, it is widely believed that cancers may share common mechanisms. In this study, we introduce a novel strategy based on multi-tasking learning methods to predict core cancer genes shared by multiple cancers in the hope of elucidating common cancer mechanisms. Our strategy uses two multi-tasking learning algorithms, one for feature selection and the other for validation of selected features. The combined use of two methods results in more robust classifiers and reliable selected features. The top 73 significant features, mapped to 72 genes, are selected as core cancer genes. The effectiveness of the 73 features is further demonstrated in a blind test conducted on an independent test data. The biological significance of these genes is evaluated using systems biology analyses. Extensive functional, pathway and network analysis confirms findings in previous studies and brings new insights into common cancer mechanisms. Our strategy can be used as a general method to find important genes from large gene expression datasets on the genomic level. The selected genes can be used to predict cancers. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multi-task learning Classification Core cancer genes Gene differential expression microarray data

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