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Partial maximum correlation information: A new feature selection method for microarray data classification

NEUROCOMPUTING 2019年 323卷 231-243页

作者： Yuan, Mingshun Yang, Zijiang Ji, Guoli Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China York Univ Sch Informat Technol Toronto ON M3J 1P3 Canada Xiamen Univ Innovat Ctr Cell Signaling Network Xiamen 361102 Fujian Peoples R China

Feature (gene) selection of microarray data is a very important and challenging task. This paper proposes a new feature selection method, partial maximum correlation information (PMCI), for microarray data classification. The proposed method extracts several orthogonal components from the feature space to evaluate the importance of each feature. In PMCI method, the component extraction is based on the correlation between feature space and class coding space. Meanwhile, this paper also provides a new generic class-encoding scheme for multi-class problems. This type of encoding scheme can ensure the smooth implementation for PMCI and has a clear geometric meaning. To validate the performance of the proposed method, this paper selects six state-of-the-art algorithms for comparison based on ten widely used microarray benchmark datasets for experiment. The experimental results show that PMCI is an effective feature selection method with good performance and lower time complexity. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Feature selection microarray data SIMPIS Partial maximum correlation information (PMCI)

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A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis

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PATTERN RECOGNITION 2019年 90卷 346-362页

作者： Sun, Mengxin Liu, Kunhong Wu, Qingqiang Hong, Qingqi Wang, Beizhan Zhang, Haiying Xiamen Univ Software Sch Xiamen 361005 Fujian Peoples R China

Nowadays, a lot of new classification and clustering techniques have been proposed for microarray data analysis. However, the multiclass microarray data classification is still regarded as a tough task because of the small sample size problem and the class imbalance problem. In this paper, we propose a novel error correcting output code (ECOC) algorithm for the classification of multiclass microarray data based on the data complexity (DC) theory. In this algorithm, an ECOC coding matrix is generated based on a hierarchical partition of the class space with the aim of Minimizing data Complexity (named as ECOC-MDC). As the partition process can be mapped as a binary tree, a compact ensemble with high discrimination power is produced. The performance of ECOC-MDC is compared with some state-of-art ECOC algorithms on six multiclass microarray data sets, and it is found that the proposed algorithm can obtain better results in most cases. The correlation between DC measures and the dichotomizers' performances is checked, and the observations confirm that high complexity in data usually leads to high error rates of the connected dichotomizers. But the error correcting mechanism in the ECOC framework can effectively improve our algorithm's generalization ability. In short, ECOC-MDC can produce a compact ensemble system with high error correction capability through the application of diverse DC measures. Our Matlab code is available at: ***/MLDMXM2017/ECOC-MDC. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Error correcting output codes (ECOC) data complexity microarray data Multiclass

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Design Exploration of Geometric Biclustering for microarray data Analysis in data Mining

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2014年第10期25卷 2540-2550页

作者： Wang, Doris Z. Cheung, Ray C. C. Yan, Hong City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Biclustering is an important technique in data mining for searching similar patterns. Geometric biclustering (GBC) method is used to reduce the complexity of the NP-complete biclustering algorithm. This paper studies three commonly used modern platforms including multi-core CPU, GPU and FPGA to accelerate this GBC algorithm. By analyzing the parallelizing property of the GBC algorithm, we design 1) a multi-threaded software running on a server grade multi-core CPU system, 2) a CUDA program for GPU to accelerate the GBC algorithm, and 3) a novel parameterizable and scalable hardware architecture implemented on an FPGA. Genes microarray pattern analysis is employed as an example to demonstrate performance comparisons on different platforms. In particular, we compare the speed and energy efficiency of the three proposed methods. We found that 1) GPU achieves the highest average speedup of 48x compared to single-threaded GBC program, 2) Our FPGA design can achieve higher speedup of 4x for the computation for large microarray, and 3) FPGA consumes the least energy, which is about 3.53x more efficient than the single-threaded GBC program.

关键词： Geometric biclustering (GBC) microarray data graphics processing unit (GPU) field-programmable gate array (FPGA)

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Constructing the gene regulation-level representation of microarray data for cancer classification

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JOURNAL OF BIOMEDICAL INFORMATICS 2008年第1期41卷 95-105页

作者： Wong, Hau-San Wang, Hong-Qiang City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

In this paper, we propose a regulation-level representation for microarray data and optimize it using genetic algorithms (GAs) for cancer classification. Compared with the traditional expression-level features, this representation can greatly reduce the dimensionality of microarray data and accommodate noise and variability such that many statistical machine-learning methods now become applicable and efficient for cancer classification. Experimental results on real-world microarray datasets show that the regulation-level representation can consistently converge at a solution with three regulation levels. This verifies the existence of the three regulation levels (up-regulation, down-regulation and non-significant regulation) associated with a particular biological phenotype. The ternary regulation-level representation not only improves the cancer classification capability but also facilitates the visualization of microarray data. (C) 2007 Elsevier Inc. All rights reserved.

关键词： cancer classification genetic algorithms gene expression levels gene regulation levels histogram microarray data

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A new multi-objective binary Harris Hawks optimization for gene selection in microarray data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第4期14卷 3157-3176页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France Lab Informat & Its Applicat Msila LIAM Msila Algeria

Cancer classification is one of the main applications of gene expression data (microarray data) and is essential for a comprehensive diagnosis of cancer treatment. Therefore, bio-inspired algorithms have developed several effective applications in the analysis of gene selection, which are one of the most effective applied in this domain. Harris Hawks optimization is a novel and recent algorithm that has an excellent balance between exploration and exploitation. This paper presents the first study on multi-objective binary Harris Hawks optimization (MOBHHO) for gene selection. We define gene selection as a problem, including two main conflicting objectives: minimizing the number of genes and maximizing the classification accuracy. MOBHHO uses two fitness functions to solve competing objectives. The first function based on SVM with LOOCV classifier and the second function also depends on KNN with K-fold classifier, as well as the percentage of gene selection found in both functions. Furthermore, MOBHHO tries to find the Pareto-optimal solutions, i.e. the best gene subset that contains a minimal number of selected genes and better classification accuracy. We have integrated several filter-based ranking methods with our proposal. In order to test the performance accuracy of the proposed MOBHHO algorithm, we compared our algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on eight benchmarks (binary-class and multi-class), MOBHHO able to provide a minimum number of genes to obtain the highest classification accuracy. The proposed method reaches above 98% classification accuracy in six benchmark datasets and a maximum accuracy of 100% is achieved.

关键词： Gene expression Feature selection Harris Hawks optimization algorithm microarray data Cancer classification Swarm intelligence

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Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 116卷 103577-103577页

作者： Kourou, Konstantina Rigas, George Papaloukas, Costas Mitsis, Michalis Fotiadis, Dimitrios I. Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Univ Ioannina Dept Biol Applicat & Technol GR-45110 Ioannina Greece Univ Ioannina Fac Med Sch Hlth Sci Dept Surg GR-45110 Ioannina Greece Univ Ioannina Fac Med Sch Hlth Sci Canc Biobank Ctr GR-45110 Ioannina Greece Fdn Res & Technol Hellas Dept Biomed Res Inst Mol Biol & Biotechnol GR-45110 Ioannina Greece

Genomic profiling of cancer studies has generated comprehensive gene expression patterns for diverse phenotypes. Computational methods which employ transcriptomics datasets have been proposed to model gene expression data. Dynamic Bayesian Networks (DBNs) have been used for modeling time series datasets and for the inference of regulatory networks. Furthermore, cancer classification through DBN-based approaches could reveal the importance of exploiting knowledge from statistically significant genes and key regulatory molecules. Although microarray datasets have been employed extensively by several classification methods for decision making, the use of new knowledge from the pathway level has not been addressed adequately in the literature in terms of DBNs for cancer classification. In the present study, we identify the genes that act as regulators and mediate the activity of transcription factors that have been found in all promoters of our differentially expressed gene sets. These features serve as potential priors for distinguishing tumor from normal samples using a DBN-based classification approach. We employed three microarray datasets from the Gene Expression Omnibus (GEO) public functional repository and performed differential expression analysis. Promoter and pathway analysis of the identified genes revealed the key regulators which influence the transcription mechanisms of these genes. We applied the DBN algorithm on selected genes and identified the features that can accurately classify the samples into tumors and controls. Both accuracy and Area Under the Curve (AUC) were high for the gene sets comprising of the differentially expressed genes along with their master regulators (accuracy: 70.8%-98.5%;AUC: 0.562-0.985).

关键词： Genomic profiling microarray data Dynamic Bayesian Networks Cancer classification Regulatory genes

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A New Evolutionary Ensemble Learning of Multimodal Feature Selection from microarray data

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NEURAL PROCESSING LETTERS 2023年第5期55卷 6753-6780页

作者： Nekouie, Nadia Romoozi, Morteza Esmaeili, Mahdi Islamic Azad Univ Dept Comp Engn Kashan Branch Kashan Iran

In the last decades, data has grown exponentially with respect to the number of samples and features. This makes the feature selection (FS) more challenging. In this paper, an optimization method called the multimodal optimization (MMO) technique is employed to find multiple optimal solutions instead of a single solution. The main contribution of the MMO technique is to provide multiple optimal solutions, instead of a single solution. Using the hidden information in the data and creating an ensemble of classifiers, the potential and information of multiple answers provided by MMO are used to address the issue of FS from microarray data. After pre-processing of the data, to benefit from the potential and information of multiple answers, the optimal features subset are obtained by a firefly-based MMO algorithm. The mutual information method is used as the fitness function to evaluate the proposed subset of features. Then, each feature subset is used to train a classifier and the classifiers are trained by the data, the features of which are presented by a MMO algorithm, and these classifiers make an ensemble. To select a proper combination, a particle swarm optimization algorithm is used. Finally, the algorithm for the datasets of the microarray is evaluated in terms of cancer diagnosis. The proposed method efficiency is evaluated by applying on 11 datasets. The results indicate the superiority and proper performance of the multimodal FS method compared to other methods.

关键词： Feature selection Multimodal optimization microarray data Multimodal feature selection Ensemble of classifiers

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Can classification performance be predicted by complexity measures? A study using microarray data

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第3期51卷 1067-1090页

作者： Moran-Fernandez, L. Bolon-Canedo, V. Alonso-Betanzos, A. Univ A Coruna Dept Comp Sci Lab Res & Dev Artificial Intelligence LIDIA La Coruna 15071 Spain

data complexity analysis enables an understanding of whether classification performance could be affected, not by algorithm limitations, but by intrinsic data characteristics. microarray datasets based on high numbers of gene expressions combined with small sample sizes represent a particular challenge for machine learning researchers. This type of data also has other particularities that may negatively affect the generalization capacity of classifiers, such as overlaps between classes and class imbalance. Making use of several complexity measures, we analyzed the intrinsic complexity of several microarray datasets with and without feature selection and then explored the connection with the empirical results obtained by four widely used classifiers. Experimental results for 21 binary and multiclass datasets demonstrate that a correlation exists between microarray data complexity and the classification error rates.

关键词： data complexity measures Classification microarray data Feature selection Filters

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A reductionist approach to extract robust molecular markers from microarray data series - Isolating markers to track osseointegration

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 68卷 104-111页

作者： Barik, Anwesha Banerjee, Satarupa Dhara, Santanu Chakravorty, Nishant Indian Inst Technol Sch Med Sci & Technol Kharagpur 721302 W Bengal India

Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured;MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Osseointegration microarray data Statistical classifiers Gene ontology Surface texture

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Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第16期39卷 12371-12377页

作者： Nahar, Jesmin Imam, Tasadduq Tickle, Kevin S. Ali, A. B. M. Shawkat Chen, Yi-Ping Phoebe Cent Queensland Univ Fac Arts Business Informat & Educ Rockhampton Qld 4702 Australia La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic 3086 Australia

The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Breast cancer microarray data Image data Computational intelligence SMOTE SMO

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