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Stephan S, Galland S, Labbani Narsis O, Shoji K, Vachenc S, Gerart S, Nicolle C. Agent-based approaches for biological modeling in oncology: A literature review. Artif Intell Med 2024; 152:102884. [PMID: 38703466 DOI: 10.1016/j.artmed.2024.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
CONTEXT Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.
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Affiliation(s)
- Simon Stephan
- UTBM, CIAD UMR 7533, Belfort, F-90010, France; Université de Bourgogne, CIAD UMR 7533, Dijon, F-21000, France.
| | | | | | - Kenji Shoji
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Sébastien Vachenc
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Stéphane Gerart
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
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Abd-elnaby M, Alfonse M, Roushdy M. A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer. DIGITAL TRANSFORMATION TECHNOLOGY 2022:547-560. [DOI: 10.1007/978-981-16-2275-5_36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Azadifar S, Ahmadi A. A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm. BMC Med Inform Decis Mak 2021; 21:333. [PMID: 34838034 PMCID: PMC8627636 DOI: 10.1186/s12911-021-01696-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Abstract
Background Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data mining and machine learning algorithms. One of the most effective approaches to alleviate this problem is to use gene selection methods. The aim of gene selection is to reduce the dimensions (features) of gene expression data leading to eliminating irrelevant and redundant genes. Methods This paper presents a hybrid gene selection method based on graph theory and a many-objective particle swarm optimization (PSO) algorithm. To this end, a filter method is first utilized to reduce the initial space of the genes. Then, the gene space is represented as a graph to apply a graph clustering method to group the genes into several clusters. Moreover, the many-objective PSO algorithm is utilized to search an optimal subset of genes according to several criteria, which include classification error, node centrality, specificity, edge centrality, and the number of selected genes. A repair operator is proposed to cover the whole space of the genes and ensure that at least one gene is selected from each cluster. This leads to an increasement in the diversity of the selected genes. Results To evaluate the performance of the proposed method, extensive experiments are conducted based on seven datasets and two evaluation measures. In addition, three classifiers—Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—are utilized to compare the effectiveness of the proposed gene selection method with other state-of-the-art methods. The results of these experiments demonstrate that our proposed method not only achieves more accurate classification, but also selects fewer genes than other methods. Conclusion This study shows that the proposed multi-objective PSO algorithm simultaneously removes irrelevant and redundant features using several different criteria. Also, the use of the clustering algorithm and the repair operator has improved the performance of the proposed method by covering the whole space of the problem.
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Affiliation(s)
- Saeid Azadifar
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Ali Ahmadi
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
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Li HD, Xu Y, Zhu X, Liu Q, Omenn GS, Wang J. ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets. J Bioinform Comput Biol 2021; 18:2040009. [PMID: 32698720 DOI: 10.1142/s0219720020400090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php.
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Affiliation(s)
- Hong-Dong Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
| | - Yunpei Xu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
| | - Xiaoshu Zhu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China.,School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi, P. R. China
| | - Quan Liu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
| | - Gilbert S Omenn
- Departments of Computational Medicine and Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 400083, P. R. China
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Peng C, Wu X, Yuan W, Zhang X, Zhang Y, Li Y. MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:621-632. [PMID: 31180870 DOI: 10.1109/tcbb.2019.2921961] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Microarray gene expression data have become a topic of great interest for cancer classification and for further research in the field of bioinformatics. Nonetheless, due to the "large p, small n" paradigm of limited biosamples and high-dimensional data, gene selection is becoming a demanding task, which is aimed at selecting a minimal number of discriminatory genes associated closely with a phenotype. Feature or gene selection is still a challenging problem owing to its nondeterministic polynomial time complexity and thus most of the existing feature selection algorithms utilize heuristic rules. A multilayer recursive feature elimination method based on an embedded integer-coded genetic algorithm, MGRFE, is proposed here, which is aimed at selecting the gene combination with minimal size and maximal information. On the basis of 19 benchmark microarray datasets including multiclass and imbalanced datasets, MGRFE outperforms state-of-the-art feature selection algorithms with better cancer classification accuracy and a smaller selected gene number. MGRFE could be regarded as a promising feature selection method for high-dimensional datasets especially gene expression data. Moreover, the genes selected by MGRFE have close biological relevance to cancer phenotypes. The source code of our proposed algorithm and all the 19 datasets used in this paper are available at https://github.com/Pengeace/MGRFE-GaRFE.
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Tabares-Soto R, Orozco-Arias S, Romero-Cano V, Segovia Bucheli V, Rodríguez-Sotelo JL, Jiménez-Varón CF. A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data. PeerJ Comput Sci 2020; 6:e270. [PMID: 33816921 PMCID: PMC7924492 DOI: 10.7717/peerj-cs.270] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 03/08/2020] [Indexed: 05/06/2023]
Abstract
Cancer classification is a topic of major interest in medicine since it allows accurate and efficient diagnosis and facilitates a successful outcome in medical treatments. Previous studies have classified human tumors using a large-scale RNA profiling and supervised Machine Learning (ML) algorithms to construct a molecular-based classification of carcinoma cells from breast, bladder, adenocarcinoma, colorectal, gastro esophagus, kidney, liver, lung, ovarian, pancreas, and prostate tumors. These datasets are collectively known as the 11_tumor database, although this database has been used in several works in the ML field, no comparative studies of different algorithms can be found in the literature. On the other hand, advances in both hardware and software technologies have fostered considerable improvements in the precision of solutions that use ML, such as Deep Learning (DL). In this study, we compare the most widely used algorithms in classical ML and DL to classify the tumors described in the 11_tumor database. We obtained tumor identification accuracies between 90.6% (Logistic Regression) and 94.43% (Convolutional Neural Networks) using k-fold cross-validation. Also, we show how a tuning process may or may not significantly improve algorithms' accuracies. Our results demonstrate an efficient and accurate classification method based on gene expression (microarray data) and ML/DL algorithms, which facilitates tumor type prediction in a multi-cancer-type scenario.
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Affiliation(s)
- Reinel Tabares-Soto
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
| | - Simon Orozco-Arias
- Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Department of Systems and informatics, Universidad de Caldas, Manizales, Caldas, Colombia
| | - Victor Romero-Cano
- Department of Automatics and Electronics, Universidad Autónoma de Occidente, Cali, Valle del Cauca, Colombia
| | - Vanesa Segovia Bucheli
- İzmir International Biomedicine and Genome Institute, Dokuz Eylül University, Izmir, Turkey
| | - José Luis Rodríguez-Sotelo
- Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
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Alanni R, Hou J, Azzawi H, Xiang Y. Deep gene selection method to select genes from microarray datasets for cancer classification. BMC Bioinformatics 2019; 20:608. [PMID: 31775613 PMCID: PMC6880643 DOI: 10.1186/s12859-019-3161-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 10/15/2019] [Indexed: 12/15/2022] Open
Abstract
Background Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis. Results The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost. Conclusions We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples’ classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.
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Affiliation(s)
- Russul Alanni
- School of Information Technology, Deakin University, Geelong, Victoria, Australia.
| | - Jingyu Hou
- School of Information Technology, Deakin University, Geelong, Victoria, Australia
| | - Hasseeb Azzawi
- School of Information Technology, Deakin University, Geelong, Victoria, Australia
| | - Yong Xiang
- School of Information Technology, Deakin University, Geelong, Victoria, Australia
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Alanni R, Hou J, Azzawi H, Xiang Y. A novel gene selection algorithm for cancer classification using microarray datasets. BMC Med Genomics 2019; 12:10. [PMID: 30646919 PMCID: PMC6334429 DOI: 10.1186/s12920-018-0447-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 12/07/2018] [Indexed: 12/18/2022] Open
Abstract
Background Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results. Methods An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP. Results Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods. Conclusion Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.
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Affiliation(s)
- Russul Alanni
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia.
| | - Jingyu Hou
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
| | - Hasseeb Azzawi
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
| | - Yong Xiang
- School of Information Technology, Deakin University, Burwood, 3125, VIC, Australia
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