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Speeding up the discovery of combinations of differentially expressed genes for disease prediction and classification

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019年 170卷 69-80页

作者： Khamesipour, Alireza Kagaris, Dimitri Southern Illinois Univ ECE Dept Carbondale IL 62901 USA

Background and objective: Finding combinations (i.e., pairs, or more generally, q-tuples with q >= 2) of genes whose behavior as a group differs significantly between two classes has received a lot of attention in the quest for the discovery of simple, accurate, and easily interpretable decision rules for disease classification and prediction. For example, the Top Scoring Pair (TSP) method seeks to find pairs of genes so that the probability of the reversal of the relative ranking of the expression levels of the genes in the two classes is maximized. The computational cost of finding a q-tuple of genes that scores highest under a given metric is O(G(q)), where G is the total number of genes. This cost is often problematic or prohibitive in practice (even for q = 2), as the number of genes G is often in the order of tens of thousands. Methods: In this paper, we show that this computational cost can be significantly reduced by excluding from consideration genes whose behavior is almost identical in the two classes and therefore their inclusion in any q-tuple is rather non-informative. Our criterion for the exclusion of genes is supported by a statistically robust metric, the Area Under the Curve (AUC) of the corresponding Receiver Operating Characteristic (ROC) curve. By filtering out genes whose AUC value is below a user-chosen threshold, as determined by a procedure that we describe in the paper, dramatic reductions in the run times are obtained while maintaining the same classification accuracy. Results: We have experimentally verified the gains of this approach on several case studies involving ovarian, colon, leukemia, breast and prostate cancers, and diffuse large b-cell lymphoma. Conclusions: The proposed method is not only faster (for example, we observed an average 78.65% reduction over the run time of TSP) while maintaining the same classification accuracy, but it can even result in better classification accuracy due to its inherent ability to avoid the so-c

关键词： microarray data analysis Gene expression Top Scoring Pair (TSP) Computational cost Cancer prediction

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Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

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BMC BIOINFORMATICS 2016年第Sup1期17卷 13-26页

作者： Huang, Chien-Hung Chang, Peter Mu-Hsin Hsu, Chia-Wei Huang, Chi-Ying F. Ng, Ka-Lok Natl Formosa Univ Dept Comp Sci & Informat Engn Huwei 63205 Taiwan Taipei Vet Gen Hosp Dept Med Div Hematol & Oncol Taipei 112 Taiwan Natl Yang Ming Univ Fac Med Taipei 112 Taiwan Natl Yang Ming Univ Inst Biopharmaceut Sci Taipei 112 Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung 41354 Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung 40402 Taiwan

Background: Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. Results: This work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early-and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. Conclusions: With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.

关键词： Non-small cell lung cancer Drug repositioning microarray data analysis Machine learning algorithm Topological parameters Protein-protein interactions Enrichment analysis Connectivity Map

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Natural computing methods in bioinformatics: A survey

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INFORMATION FUSION 2009年第3期10卷 211-216页

作者： Masulli, Francesco Mitra, Sushmita Univ Genoa Dept Comp & Informat Sci I-16146 Genoa Italy Temple Univ Ctr Biotechnol Philadelphia PA 19122 USA Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Often data analysis problems in Bioinformatics concern the fusion of multisensor outputs or the fusion of multisource information, where one must integrate different kinds of biological data. Natural computing provides several possibilities in Bioinformatics, especially by presenting interesting nature-inspired methodologies for handling such complex problems. In this article we survey the role of natural computing in the domains of protein structure prediction, microarray data analysis and gene regulatory network generation. We utilize the learning ability of neural networks for adapting, uncertainty handling capacity of fuzzy sets and rough sets for modeling ambiguity, and the search potential of genetic algorithms for efficiently traversing large search spaces. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Natural computing Bioinformatics Protein structure prediction microarray data analysis Biological networks modeling

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A recursive network approach can identify constitutive regulatory circuits in gene expression data

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2005年第348期348卷 349-370页

作者： Blasi, MF Casorelli, I Colosimo, A Blasi, FS Bignami, M Giuliani, A Ist Super Sanita Dept Environm & Primary Prevent I-00161 Rome Italy Univ Roma La Sapienza Dept Human Physiol & Pharmacol Rome Italy Univ Roma Tor Vergata Dept Math I-00173 Rome Italy

The activity of the cell is often coordinated by the organisation of proteins into regulatory circuits that share a common function. Genome-wide expression profiles might contain important information on these circuits. Current approaches for the analysis of gene expression data include clustering the individual expression measurements and relating them to biological functions as well as modelling and simulation of gene regulation processes by additional computer tools. The identification of the regulative programmes from microarray experiments is limited, however, by the intrinsic difficulty of linear methods to detect low-variance signals and by the sensitivity of the different approaches. Here we face the problem of recognising invariant patterns of correlations among gene expression reminiscent of regulation circuits. We demonstrate that a recursive neural network approach can identify genetic regulation circuits from expression data for ribosomal and genome stability genes. The proposed method, by greatly enhancing the sensitivity of microarray studies, allows the identification of important aspects of genetic regulation networks and might be useful for the discrimination of the different players involved in regulation circuits. Our results suggest that the constitutive regulatory networks involved in the generic organisation of the cell display a high degree of clustering depending on a modular architecture. (C) 2004 Elsevier B.V. All rights reserved.

关键词： genetic networks constitutive circuits gene expression microarray data analysis computational biology

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Inferring Large-Scale Gene Regulatory Networks Using a Randomized Algorithm Based on Singular Value Decomposition

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第6期16卷 1997-2008页

作者： Fan, Anjing Wang, Haitao Xiang, Hua Zou, Xiufen Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China

Reconstructing large-scale gene regulatory networks (GRNs) is a challenging problem in the field of computational biology. Various methods for inferring GRNs have been developed, but they fail to accurately infer GRNs with a large number of genes. Additionally, the existing evaluation indexes for evaluating the constructed networks have obvious disadvantages because GRNs in most biological systems are sparse. In this paper, we develop a new method for inferring GRNs based on randomized singular value decomposition (RSVD) and ordinary differential equation (ODE)-based optimization, denoted as IGRSVD, from large-scale time series data with noise. The three major contributions of this paper are as follows. First, the IGRSVD algorithm uses the RSVD to handle the noise and reduce the original large-scale data into small-scale problems. Second, we propose two new evaluated indexes, the expected value accuracy (EVA) and the expected value error (EVE), to evaluate the performance of inferred networks by considering the sparse features in the network. Finally, the proposed IGRSVD algorithm is compared with the existing SVD algorithm and PCA_CMI algorithm using four subsets from E. coli and datasets from DREAM challenge. The experimental results demonstrate that the IGRSVD algorithm is effective and more suitable for reconstructing large-scale networks.

关键词： Gene regulatory networks microarray data analysis reconstruction randomized singular value decomposition

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Biclustering algorithms for biological data analysis: A survey

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2004年第1期1卷 24-45页

作者： Madeira, SC Oliveira, AL Univ Beira Interior P-6200001 Covilha Portugal INESC ID Lisbon Portugal Univ Tecn Lisboa Inst Super Tecn P-1000029 Lisbon Portugal

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.

关键词： biclustering simultaneous clustering coclustering subspace clustering bidimensional clustering direct clustering block clustering two-way clustering two-mode clustering two-sided clustering microarray data analysis biological data analysis gene expression data

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Cancer Classification from Gene Expression data by NPPC Ensemble

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第3期8卷 659-671页

作者： Ghorai, Santanu Mukherjee, Anirban Sengupta, Sanghamitra Dutta, Pranab K. MCKV Inst Engn Dept Elect & Commun Engn Howrah 711204 W Bengal India Indian Inst Technol Dept Elect Engn Kharagpur 721302 W Bengal India Univ Calcutta Dept Biochem Kolkata 700019 W Bengal India

The most important application of microarray in gene expression analysis is to classify the unknown tissue samples according to their gene expression levels with the help of known sample expression levels. In this paper, we present a nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a computer-aided diagnosis (CAD) framework than that of a single NPPC model. For each data set only, a few genes are selected by using a mutual information criterion. Then a genetic algorithm-based simultaneous feature and model selection scheme is used to train a number of NPPC expert models in multiple subspaces by maximizing cross-validation accuracy. The members of the ensemble are selected by the performance of the trained models on a validation set. Besides the usual majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. The effectiveness of the NPPC ensemble and the proposed new approach of combining decisions for cancer diagnosis are studied and compared with support vector machine (SVM) classifier in a similar framework. Experimental results on cancer data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training time on average.

关键词： Cancer classification classifier ensemble combination of multiple classifiers microarray data analysis proximal classifier

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Advances in metaheuristics for gene selection and classification of microarray data

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BRIEFINGS IN BIOINFORMATICS 2010年第1期11卷 127-141页

作者： Duval, Beatrice Hao, Jin-Kao Univ Angers F-49045 Angers 01 France

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded approach for gene selection. In particular, we put forward the importance and usefulness of integrating problem-specific knowledge into the search operators of such a method. To illustrate the point, we explain how ranking coefficients of a linear classifier such as support vector machine (SVM) can be profitably used to reinforce the search efficiency of Local Search and Evolutionary Search metaheuristic algorithms for gene selection and classification.

关键词： microarray data analysis gene selection classification local search genetic algorithm memetic algorithm

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Some statistics in bioinformatics: The fifth Armitage Lecture

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STATISTICS IN MEDICINE 2009年第23期28卷 2833-2856页

作者： Solomon, Patricia J. Univ Adelaide Sch Math Sci Adelaide SA 5005 Australia

The spirit and content of the 2007 Armitage Lecture are presented in this paper. To begin, two areas of Peter Armitage's early work are distinguished: his pioneering research on sequential methods intended for use in medical trials and the comparison of survival curves. Their influence on Much later work is highlighted, and motivate the proposal of several statistical 'truths' that are presented in the paper. The illustration of these truths demonstrates biology's new morphology and its dominance over statistics in this century. An overview of a recent proteomics ovarian cancer study is given as a warning of what can happen when bioinformatics meets epidemiology badly, in particular, when the study design is poor. A statistical bioinformatics success story is outlined, in which gene profiling is helping to identify novel genes and networks involved in mouse embryonic stem cell development. Some concluding thoughts are given. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： sequential trial survival analysis some statistical truths AIDS epidemiology microarray data analysis bioinformatics serum proteomic test for ovarian cancer embryonic stem cells pluripotency gene expression gene profiling proteomic profiles for ovarian cancer

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Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts

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GENOMICS 2017年第2期109卷 91-107页

作者： Dashtban, M. Balafar, Mohammadali Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of clataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high -dimensional cancer datasets;for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Gene selection Cancer classification microarray data analysis Intelligent Dynamic Algorithm Random-restart hill climbing Reinforcement learning Penalizing strategy Cut and splice crossover Self-refinement strategy Feature selection

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