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Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

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TOXICOLOGY AND APPLIED PHARMACOLOGY 2013年第3期271卷 386-394页

作者： Ovacik, Meric A. Sen, Banalata Euling, Susan Y. Gaido, Kevin W. Ierapetritou, Marianthi G. Androulakis, Ioannis P. Rutgers State Univ Chem & Biochem Engn Dept Piscataway NJ 08854 USA US EPA Natl Ctr Environm Assessment Res Triangle Pk NC 27709 USA US EPA Natl Ctr Environm Assessment Off Res & Dev Washington DC 20460 USA US FDA Ctr Vet Med Off New Anim Drug Evaluat Div Human Food Safety Rockville MD 20855 USA Rutgers State Univ Dept Biomed Engn Piscataway NJ 08854 USA

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data. (C) 2010 Published by Elsevier Inc.

关键词： Dibutyl Phthalate (DBP) microarray data Pathway analysis Singular value decomposition (SVD) Time course

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Revisiting Link-Based Cluster Ensembles for microarray data Classification

Revisiting Link-Based Cluster Ensembles for Microarray Data ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Iam-On, Natthakan Boongoen, Tossapon Mae Fah Luang Univ Sch Informat Technol Chiang Rai 57100 Thailand Royal Thai Air Force Acad Dept Math & Comp Sci Bangkok 10220 Thailand

ISBN: (纸本)9781479906529

Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. microarray technology provides a useful basis of achieving this ultimate goal. In particular to cancer research, it has become almost routine to create gene expression profiles, which can discriminate patients into good and poor prognosis groups, and identify possible tumor subtypes. This classification or predictive model offers a useful tool for individualized treatment of disease. However, the accuracy of existing classifiers have been constrained by the curse of dimensionality typically observed in microarray data. In addition to gene selection, one may transform the original data to another variation, where only key gene components are included. Unlike conventional transformation-based techniques found in the literature, this paper presents a novel method that makes use of cluster ensembles, specifically the summarizing information matrix, as the transformed data for the following classification step. Among different state-of-the-art methods, the link-based cluster ensemble approach (LCE) provides a highly accurate clustering, and thus particularly employed here. The performance of this transformation model is evaluated on published microarray datasets and C4.5, in comparison with benchmark techniques. The findings suggest that the new model can improve the classification accuracy of original data and performs better than the other transformation methods investigated in the empirical study.

关键词： microarray data classification cluster ensembles link-based similarity

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Identification of Triple Negative Breast Cancer Genes Using Rough Set Based Feature Selection Algorithm & Ensemble Classifier

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2022年第54期12卷

作者： Patil, Sujata Balmuri, Kavitha Rani Frnda, Jaroslav Parameshachari, B. D. Konda, Srinivas Nedoma, Jan KLE Dr MS Sheshgiri Coll Engn & Technol Dept ECE Belgaum India CMR Tech Campus Dept Informat Technol Hyderabad Telangana India Univ Zilina Fac Operat & Econ Transport & Commun Dept Quantitat Methods & Econ Informat Zilina Slovakia VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Telecommun Ostrava Czech Republic Nitte Meenakshi Inst Technol Dept Elect & Commun Engn Bengaluru India CMR Tech Campus Dept Comp Sci Engn Hyderabad India

In recent decades, microarray datasets have played an important role in triple negative breast cancer (TNBC) detection. microarray data classification is a challenging process due to the presence of numerous redundant and irrelevant features. Therefore, feature selection becomes irreplaceable in this research field that eliminates non-required feature vectors from the system. The selection of an optimal number of features significantly reduces the NP hard problem, so a rough set-based feature selection algorithm is used in this manuscript for selecting the optimal feature values. Initially, the datasets related to TNBC are acquired from gene expression omnibuses like GSE45827, GSE76275, GSE65194, GSE3744, GSE21653, and GSE7904. Then, a robust multi-array average technique is used for eliminating the outlier samples of TNBC/non-TNBC which helps enhancing classification performance. Further, the pre-processed microarray data are fed to a rough set theory for optimal gene selection, and then the selected genes are given as the inputs to the ensemble classification technique for classifying low-risk genes (non-TNBC) and high-risk genes (TNBC). The experimental evaluation showed that the ensemble-based rough set model obtained a mean accuracy of 97.24%, which superior related to other comparative machine learning techniques.

关键词： Ensemble Classifier Machine-Learning Technique microarray data Robust Multi-Array Average Technique Rough Set Theory Triple Negative Breast Cancer

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Identifying functionally relevant candidate genes for inflexible ethanol intake in mice and humans using a guilt-by-association approach

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BRAIN AND BEHAVIOR 2020年第12期10卷 e01879页

作者： Martins de Carvalho, Luana A. S. Fonseca, Pablo M. Paiva, Isadora Damasceno, Samara S. B. Pedersen, Agatha da Silva e Silva, Daniel E. Wiers, Corinde D. Volkow, Nora Brunialti Godard, Ana L. Univ Fed Minas Gerais UFMG Dept Genet Lab Genet Anim & Humana Belo Horizonte MG Brazil NIAAA Lab Neuroimaging NIH Bethesda MD USA Univ Illinois Dept Psychiat Ctr Alcohol Res Epigenet Chicago IL 60612 USA Univ Fed Minas Gerais UFMG Dept Genet Lab Genet Humana & Med Belo Horizonte MG Brazil Univ Guelph Dept Anim Biosci Ctr Genet Improvement Livestock Guelph ON Canada NIAAA Lab Neurobiol Compuls Behav NIH Bethesda MD USA NIDA NIH Bethesda MD 20892 USA

Gene prioritization approaches are useful tools to explore and select candidate genes in transcriptome studies. Knowing the importance of processes such as neuronal activity, intracellular signal transduction, and synapse plasticity to the development and maintenance of compulsive ethanol drinking, the aim of the present study was to explore and identify functional candidate genes associated with these processes in an animal model of inflexible pattern of ethanol intake. To do this, we applied a guilt-by-association approach, using the GUILDify and ToppGene software, in our previously published microarray data from the prefrontal cortex (PFC) and striatum of inflexible drinker mice. We then tested some of the prioritized genes that showed a tissue-specific pattern in postmortem brain tissue (PFC and nucleus accumbens (NAc)) from humans with alcohol use disorder (AUD). In the mouse brain, we prioritized 44 genes in PFC and 26 in striatum, which showed opposite regulation patterns in PFC and striatum. The most prioritized of them (i.e.,Plcb1 and Prkcbin PFC, andDnm2andLrrk2in striatum) were associated with synaptic neuroplasticity, a neuroadaptation associated with excessive ethanol drinking. The identification of transcription factors among the prioritized genes suggests a crucial role forIrf4in the pattern of regulation observed between PFC and striatum. Lastly, the differential transcription ofIRF4andLRRK2in PFC and nucleus accumbens in postmortem brains from AUD compared to control highlights their involvement in compulsive ethanol drinking in humans and mice.

关键词： alcohol use disorders GUILDify IRF4 LRRK microarray data prefrontal cortex striatum ToppGene OR guilt-by-association approaches

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Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

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IET SYSTEMS BIOLOGY 2020年第2期14卷 85-95页

作者： Baliarsingh, Santos Kumar Vipsita, Swati Int Inst Informat Technol Dept Comp Sci & Engn DST FIST Bioinformat Lab Bhubaneswar Odisha India

microarray technology plays a significant role in cancer classification, where a large number of genes and samples are simultaneously analysed. For the efficient analysis of the microarray data, there is a great demand for the development of intelligent techniques. In this article, the authors propose a novel hybrid technique employing Fisher criterion, ReliefF, and extreme learning machine (ELM) based on the principle of chaotic emperor penguin optimisation algorithm (CEPO). EPO is a recently developed metaheuristic method. In the proposed method, initially, Fisher score and ReliefF are independently used as filters for relevant gene selection. Further, a novel population-based metaheuristic, namely, CEPO was proposed to pre-train the ELM by selecting the optimal input weights and hidden biases. The authors have successfully conducted experiments on seven well-known data sets. To evaluate the effectiveness, the proposed method is compared with original EPO, genetic algorithm, and particle swarm optimisation-based ELM along with other state-of-the-art techniques. The experimental results show that the proposed framework achieves better accuracy as compared to the state-of-the-art schemes. The efficacy of the proposed method is demonstrated in terms of accuracy, sensitivity, specificity, and F-measure.

关键词： genetic algorithms pattern classification biology computing cancer learning (artificial intelligence) search problems particle swarm optimisation optimal input weights data sets original EPO genetic algorithm particle swarm optimisation-based ELM microarray cancer classification microarray technology microarray data intelligent techniques Fisher criterion ReliefF chaotic emperor penguin optimisation algorithm CEPO recently developed metaheuristic method Fisher score relevant gene selection population-based chaotic penguin optimised extreme learning machine F

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microarray-Based Quality Assessment as a Supporting Criterion for de novo Transcriptome Assembly Selection

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第1期17卷 198-206页

作者： Carvajal-Lopez, Patricia Von Borstel, Fernando D. Torres, Amada Rustici, Gabriella Gutierrez, Joaquin Romero-Vivas, Eduardo Ctr Invest Biol Noroeste Bioinformat Res Grp La Paz Bcs Mexico Univ Cambridge Genet Dept Cambridge CB2 1TN England

RNA-Sequencing and de novo assembly have enabled the analysis of species with non-available reference transcriptomes, although intrinsic features (biological and technical) induce errors in the reconstruction. A strategy to resolve these errors consists of varying assembling process parameters to generate multiple reconstructions. However, the best assembly selection remains a challenge. Quantitative metrics for quality assessment have been inconsistent when compared with pertinent references. In this paper, a criterion for supporting assembly selection based on mapping DNA microarray hybridized probes to assembly sets is proposed. Mouse and fruit fly RNA-Seq datasets were assembled with standard de novo procedures. Quality assessment was estimated using quantitative metrics and the proposed criterion. The assembly that best mapped to the available reference transcriptomes of these model species provided the highest quality assembly. The hybridized probes identified the best assemblies, whereas quantitative metrics remained inconsistent. For example, subtle probe mapping difference of 0.25 percent, but statistically significant (ANOVA, p < 0.05), enabled the assembly selection that led to identify 3,719 more contigs and led to 1,049 further mapped contigs to the mouse reference transcriptome. The microarray data availability for non-model species makes the proposed criterion suitable for quality assessment of multiple de novo assembly strategies.

关键词： Quality assessment Probes Mice Bioinformatics Genomics microarray data RNA-Seq quality assessment next-generation sequencing (NGS)

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Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection

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IEEE ACCESS 2020年 8卷 75393-75408页

作者： Chatterjee, Bitanu Bhattacharyya, Trinav Ghosh, Kushal Kanti Singh, Pawan Kumar Geem, Zong Woo Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Jadavpur Univ Dept Informat Technol Kolkata 700106 India Gachon Univ Dept Energy IT Seongnam 13120 South Korea

Feature selection (FS) is mainly used as a pre-processing tool to reduce dimensionality by eliminating irrelevant or redundant features to be used for a machine learning or data mining algorithm. In this paper, we have introduced binary variant of a recently proposed meta-heuristic algorithm called Social Ski Driver (SSD) optimization. To the best of our knowledge, SSD has not been used yet in the domain of FS. Two binary variants of SSD are proposed using S-shaped and V-shaped transfer functions. Besides, the exploitation ability of SSD is improved by using a local search method, called Late Acceptance Hill Climbing (LAHC). The hybrid meta-heuristic is then converted to binary version by using said transfer functions. The proposed methods are applied on 18 standard UCI datasets and compared with 15 state-of-the-art FS methods. Also to check the robustness of the proposed method, we have applied it to 3 high dimensional microarray datasets and compared with 6 state-of-the-art methods. Achieved results confirm the superiority of the proposed methods compared to other meta-heuristic wrapper based FS methods considered here. Source code of this work is available at https://***/consigliere19/SSD-LAHC.

关键词： Optimization Feature extraction Heuristic algorithms Genetic algorithms Transfer functions Machine learning Machine learning algorithms Social ski driver optimization feature selection late acceptance hill climbing UCI dataset meta-heuristic optimization microarray data

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Genomic signal processing of microarrays for cancer gene expression and identification using cluster-fuzzy adaptive networking

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SOFT COMPUTING 2020年第24期24卷 18447-18462页

作者： Mishra, Purnendu Bhoi, Nilamani Veer Surendra Sai Univ Technol Dept Elect & Tele Commun Engn Burla Odisha India

Genomic signal processing (GSP) is a functioning exploration area of recent times and a settled technique of digital signal processing for gathering information from genomic sequences. The recognition and identification of biological signals and analysis of sequences are the fundamental objectives of using GSP. microarray data are typically used in GSP;microarray study decides genes that cause a specific disease and helps in anticipating and diagnosing a disease, and characterization of diseases. microarray information is incredible innovation where information handled to an enormous number with plenty of genes. Recent research works show that microarray handling will be helpful for the classification of cancer genes. Different machine learning and artificial intelligence techniques are likewise used to distinguish the tumours and cancer cells. In this examination, the genomic signal processing is carried out utilizing cluster-fuzzy adaptive networking techniques. The major purpose of this research work is to evaluate the microarray data sets for recognizing the cancer genes. The microarray data set is generated using leukaemia, colon, prostate, breast cancer and lymphoma. Initially, the noise in the microarray is filtered and smoothened by utilizing a Kalman filter followed by an optimal clustering technique such as grid density-based clustering that is applied for clustering the microarray data sets. The clustered data of microarray are classified by adaptive neuro fuzzy interference system (ANFIS) for gene sequencing process of cancer identification. The adaptive network systems are developed based on autonomous networking concepts to change the static system into a dynamic. The efficiency of clustering is evaluated in terms of cluster indexes namely partition entropy, partition coefficient, Xie and Beni. The presented ANFIS is assessed in terms of precision, accuracy, recall, sensitivity, F-score and specificity. The proposed initiated methodology is mathematica

关键词： Genomic signal processing microarray data Kalman filter Grid density-based clustering Fuzzy interference system and partition coefficient

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Two-stage false discovery rate in microarray studies

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2020年第4期49卷 894-908页

作者： Kang, Joonsung Gangneung Wonju Natl Univ Dept Informat Stat Jukheon Gil 7 Gangneung Si South Korea

In microarray and other genomic studies, in view of an abundance of genes, one statistical approach is to hold the family wise error rate to a prescribed limit while controlling the false discovery rate by suitable multiple hypothesis testing procedures, thus generally compromising the power properties to a certain extent. Since the genes are not generally independent or even marginally identically distributed, model flexibility is an essential task regardless of dependent structures among genes. In this respect, incorporating a version of the Chen-Stein theorem, two-stage procedure has been considered;it seems to have better average power without much elevation of false discovery rate compared to single-stage procedure. Simulation studies and applications in microarray data models are also stressed with the methodological developments.

关键词： microarray data false discovery rate Poisson approximation average power

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Identification of Pathways and Genes Associated with Cold and Hot Properties of Chinese Materia Medica Based on Bioinformatics Analysis

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PHYTOCHEMISTRY LETTERS 2020年 38卷 70-77页

作者： Wang, Yue Zhou, Lu Zhang, Shujing Liu, Zhenzhu Li, Caiyan Gao, Yushan Sun, Yan Zheng, Fengjie Li, Yuhang Beijing Univ Chinese Med Sch Tradit Chinese Med Beijing 100029 Peoples R China

The Chinese Materia Medica can be divided into four classes: cold, cool, warm, and hot, which mainly exhibits two opposite properties: cold and hot. This study aimed to explore the scientific essence involved in the cold and hot features of Chinese Materia Medica. The microarray data of twelve cold/hot herbs with typical cold and hot properties (six for each group) were analyzed on the Gene-Cloud of Biotechnology Information. First, we compared the differences in total gene expression profiles and identified differentially expressed genes (DEGs) between two classes of herbs. Subsequently, gene ontology (GO) enrichment analysis, pathway analysis, and gene signal network analysis were performed to screen out related pathways and core genes. This study reveals that the upregulated genes in cold herbs are downregulated in hot herbs, and the genes downregulated in cold herbs were upregulated in hot herbs. The gene expression of the two types of herbs has apparent differences, and the DEGs enrich in six systems, including metabolism, immune system, nervous system, endocrine system, cell proliferation and apoptosis, and hemorheology. PRKACA, PRKACB, PRKCA, ADCY7, PIK3R1, and MAPK1 are the core genes. These results elucidated the objectivity of the differences between cold and hot herbs at a genetic level, and identified related pathways and core genes, which may be a feasible approach for solving the qua- litative and even quantitative problems of the cold and hot properties of herbs.

关键词： Chinese Materia Medica microarray data Cold and hot properties Bioinformatics Pathway Analysis

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