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Ersoz NS, Bakir-Gungor B, Yousef M. GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning. Front Genet 2023; 14:1139082. [PMID: 37671046 PMCID: PMC10476493 DOI: 10.3389/fgene.2023.1139082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 07/05/2023] [Indexed: 09/07/2023] Open
Abstract
Introduction: Identifying significant sets of genes that are up/downregulated under specific conditions is vital to understand disease development mechanisms at the molecular level. Along this line, in order to analyze transcriptomic data, several computational feature selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of diseases. In order to address this problem, biological domain knowledge-based feature selection methods have been proposed. Unlike computational gene selection approaches, these domain knowledge-based methods take the underlying biology into account and integrate knowledge from external biological resources. Gene Ontology (GO) is one such biological resource that provides ontology terms for defining the molecular function, cellular component, and biological process of the gene product. Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which has been embedded into a machine learning (ML) algorithm to select informative ontology terms. The genes annotated with the selected ontology terms have been used in the training part to carry out the classification task of the ML model. The output is an important set of ontologies for the two-class classification task applied to gene expression data for a given phenotype. Results: Our approach has been tested on 11 different gene expression datasets, and the results showed that GeNetOntology successfully identified important disease-related ontology terms to be used in the classification model. Discussion: GeNetOntology will assist geneticists and scientists to identify a range of disease-related genes and ontologies in transcriptomic data analysis, and it will also help doctors design diagnosis platforms and improve patient treatment plans.
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Affiliation(s)
- Nur Sebnem Ersoz
- Department of Bioengineering, Graduate School of Engineering and Science, Abdullah Gul University, Kayseri, Türkiye
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Türkiye
- Department of Bioengineering, Faculty of Life and Natural Sciences, Abdullah Gul University, Kayseri, Türkiye
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
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Liu Y, Yang Z, Zou X, Ma S, Liu D, Avdeev M, Shi S. Data quantity governance for machine learning in materials science. Natl Sci Rev 2023; 10:nwad125. [PMID: 37323811 PMCID: PMC10265966 DOI: 10.1093/nsr/nwad125] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/14/2023] [Accepted: 04/26/2023] [Indexed: 06/17/2023] Open
Abstract
Data-driven machine learning (ML) is widely employed in the analysis of materials structure-activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.
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Affiliation(s)
- Yue Liu
- School of Computer Engineering and Science, Shanghai University, Shanghai200444, China
- Shanghai Engineering Research Center of Intelligent Computing System, Shanghai200444, China
| | - Zhengwei Yang
- School of Computer Engineering and Science, Shanghai University, Shanghai200444, China
| | - Xinxin Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai200444, China
| | - Shuchang Ma
- School of Computer Engineering and Science, Shanghai University, Shanghai200444, China
| | - Dahui Liu
- School of Computer Engineering and Science, Shanghai University, Shanghai200444, China
| | - Maxim Avdeev
- Australian Nuclear Science and Technology Organisation, Sydney 2232, Australia
- School of Chemistry, The University of Sydney, Sydney 2006, Australia
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Wang Z, Zhou Y, Takagi T, Song J, Tian YS, Shibuya T. Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data. BMC Bioinformatics 2023; 24:139. [PMID: 37031189 PMCID: PMC10082986 DOI: 10.1186/s12859-023-05267-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 04/02/2023] [Indexed: 04/10/2023] Open
Abstract
BACKGROUND Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is "large p and small n" in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification. RESULTS This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies-Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected. CONCLUSIONS The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.
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Affiliation(s)
- Zixuan Wang
- Division of Medical Data Informatics, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.
| | - Yi Zhou
- Beijing International Center for Mathematical Research, Peking University, Beijing, 100871, China
| | - Tatsuya Takagi
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Jiangning Song
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC, 3800, Australia
| | - Yu-Shi Tian
- Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Tetsuo Shibuya
- Division of Medical Data Informatics, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
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Aziz RM. Cuckoo Search-Based Optimization for Cancer Classification: A New Hybrid Approach. J Comput Biol 2022; 29:565-584. [DOI: 10.1089/cmb.2021.0410] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
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Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Early diagnosis of autism is extremely beneficial for patients. Traditional diagnosis approaches have been unable to diagnose autism in a fast and accurate way; rather, there are multiple factors that can be related to identifying the autism disorder. The gene expression (GE) of individuals may be one of these factors, in addition to personal and behavioral characteristics (PBC). Machine learning (ML) based on PBC and GE data analytics emphasizes the need to develop accurate prediction models. The quality of prediction relies on the accuracy of the ML model. To improve the accuracy of prediction, optimized feature selection algorithms are applied to solve the high dimensionality problem of the datasets used. Comparing different optimized feature selection methods using bio-inspired algorithms over different types of data can allow for the most accurate model to be identified. Therefore, in this paper, we investigated enhancing the classification process of autism spectrum disorder using 16 proposed optimized ML models (GWO-NB, GWO-SVM, GWO-KNN, GWO-DT, FPA-NB, FPA-KNN, FPA-SVM, FPA-DT, BA-NB, BA-SVM, BA-KNN, BA-DT, ABC-NB, ABC-SVM, ABV-KNN, and ABC-DT). Four bio-inspired algorithms namely, Gray Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Bat Algorithms (BA), and Artificial Bee Colony (ABC), were employed for optimizing the wrapper feature selection method in order to select the most informative features and to increase the accuracy of the classification models. Five evaluation metrics were used to evaluate the performance of the proposed models: accuracy, F1 score, precision, recall, and area under the curve (AUC). The obtained results demonstrated that the proposed models achieved a good performance as expected, with accuracies of 99.66% and 99.34% obtained by the GWO-SVM model on the PBC and GE datasets, respectively.
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Benhar H, Idri A, Fernández-Alemán JL. Data preprocessing for heart disease classification: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105635. [PMID: 32652383 DOI: 10.1016/j.cmpb.2020.105635] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 06/24/2020] [Indexed: 06/11/2023]
Abstract
CONTEXT Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. OBJECTIVE The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. METHOD A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. RESULTS The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation.
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Affiliation(s)
- H Benhar
- Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco.
| | - A Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco; CSEHS-MSDA, Mohammed VI Polytechnic University, Benguerir, Morocco.
| | - J L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
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Santhakumar D, Logeswari S. Efficient attribute selection technique for leukaemia prediction using microarray gene data. Soft comput 2020. [DOI: 10.1007/s00500-020-04793-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Kuznetsov MS, Lisukov AN, Rizvanov AA, Tyapkina OV, Gusev OA, Rezvyakov PN, Kozlovskaya IB, Tomilovskaya ES, Nikolskiy EE, Islamov RR. Bioinformatic Study of Transcriptome Changes in the Mice Lumbar Spinal Cord After the 30-Day Spaceflight and Subsequent 7-Day Readaptation on Earth: New Insights Into Molecular Mechanisms of the Hypogravity Motor Syndrome. Front Pharmacol 2019; 10:747. [PMID: 31354476 PMCID: PMC6637859 DOI: 10.3389/fphar.2019.00747] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 06/11/2019] [Indexed: 11/13/2022] Open
Abstract
The hypogravity motor syndrome (HMS) is one of the deleterious impacts of weightlessness on the human body in orbital space missions. There is a hypothesis that disorders of musculoskeletal system as part of HMS arise in consequence of changes in spinal motor neurons. The study was aimed at bioinformatic analysis of transcriptome changes in lumbar spinal cords of mice after a 30-day spaceflight aboard biosatellite Bion-M1 (space group, S) and subsequent 7-day readaptation to the Earth’s gravity (recovery group, R) when compared with control mice (C group) housed in simulated biosatellite conditions on the Earth. Gene ontology and human phenotype ontology databases were used to detect biological processes, molecular functions, cellular components, and human phenotypes associated with HMS. Our results suggest resemblance of molecular changes developing in space orbit and during the postflight recovery to terrestrial neuromuscular disorders. Remarkably, more prominent transcriptome changes were revealed in R vs. S and R vs. C comparisons that are possibly related to the 7-day recovery period in the Earth’s gravity condition. These data may assist with establishment of HMS pathogenesis and proposing effective preventive and therapeutic options.
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Affiliation(s)
| | | | | | - Oksana Victorovna Tyapkina
- Department of Medical Biology and Genetics, Kazan State Medical University, Kazan, Russia.,Kazan Institute of Biochemistry and Biophysics, Federal Research Center "Kazan Scientific Center" of RAS, Kazan, Russia
| | - Oleg Aleksandrovich Gusev
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.,RIKEN Institute, Yokohama, Japan
| | | | | | | | - Evgeny Evgenievich Nikolskiy
- Department of Medical Biology and Genetics, Kazan State Medical University, Kazan, Russia.,Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.,Kazan Institute of Biochemistry and Biophysics, Federal Research Center "Kazan Scientific Center" of RAS, Kazan, Russia
| | - Rustem Robertovich Islamov
- Department of Medical Biology and Genetics, Kazan State Medical University, Kazan, Russia.,Kazan Institute of Biochemistry and Biophysics, Federal Research Center "Kazan Scientific Center" of RAS, Kazan, Russia
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Kang S, Song J. Robust gene selection methods using weighting schemes for microarray data analysis. BMC Bioinformatics 2017; 18:389. [PMID: 28865426 PMCID: PMC5581932 DOI: 10.1186/s12859-017-1810-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 08/27/2017] [Indexed: 11/10/2022] Open
Abstract
Background A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. Results We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. Conclusions The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis. Electronic supplementary material The online version of this article (10.1186/s12859-017-1810-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Suyeon Kang
- Department of Statistics, Ewha Womans University, Seoul, South Korea
| | - Jongwoo Song
- Department of Statistics, Ewha Womans University, Seoul, South Korea.
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