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Lai J, Chen H, Li W, Li T, Wan J. Semi-supervised feature selection via adaptive structure learning and constrained graph learning. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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52
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Qiu F, Zheng P, Heidari AA, Liang G, Chen H, Karim FK, Elmannai H, Lin H. Mutational Slime Mould Algorithm for Gene Selection. Biomedicines 2022; 10:biomedicines10082052. [PMID: 36009599 PMCID: PMC9406076 DOI: 10.3390/biomedicines10082052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 02/02/2023] Open
Abstract
A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.
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Affiliation(s)
- Feng Qiu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Pan Zheng
- Information Systems, University of Canterbury, Christchurch 8014, New Zealand
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
- Correspondence:
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Haiping Lin
- Department of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou 310018, China
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53
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Gamage HN, Chetty M, Shatte A, Hallinan J. Filter feature selection based Boolean Modelling for Genetic Network Inference. Biosystems 2022; 221:104757. [PMID: 36007675 DOI: 10.1016/j.biosystems.2022.104757] [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: 02/24/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 11/02/2022]
Abstract
The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency.
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Affiliation(s)
| | - Madhu Chetty
- Health Innovation and Transformation Centre, Federation University, Victoria, Australia
| | - Adrian Shatte
- Health Innovation and Transformation Centre, Federation University, Victoria, Australia
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54
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Zheng J, Qu H, Li Z, Li L, Tang X, Guo F. A novel autoencoder approach to feature extraction with linear separability for high-dimensional data. PeerJ Comput Sci 2022; 8:e1061. [PMID: 37547057 PMCID: PMC10403198 DOI: 10.7717/peerj-cs.1061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 07/18/2022] [Indexed: 08/08/2023]
Abstract
Feature extraction often needs to rely on sufficient information of the input data, however, the distribution of the data upon a high-dimensional space is too sparse to provide sufficient information for feature extraction. Furthermore, high dimensionality of the data also creates trouble for the searching of those features scattered in subspaces. As such, it is a tricky task for feature extraction from the data upon a high-dimensional space. To address this issue, this article proposes a novel autoencoder method using Mahalanobis distance metric of rescaling transformation. The key idea of the method is that by implementing Mahalanobis distance metric of rescaling transformation, the difference between the reconstructed distribution and the original distribution can be reduced, so as to improve the ability of feature extraction to the autoencoder. Results show that the proposed approach wins the state-of-the-art methods in terms of both the accuracy of feature extraction and the linear separabilities of the extracted features. We indicate that distance metric-based methods are more suitable for extracting those features with linear separabilities from high-dimensional data than feature selection-based methods. In a high-dimensional space, evaluating feature similarity is relatively easier than evaluating feature importance, so that distance metric methods by evaluating feature similarity gain advantages over feature selection methods by assessing feature importance for feature extraction, while evaluating feature importance is more computationally efficient than evaluating feature similarity.
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Affiliation(s)
- Jian Zheng
- College of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing, China
| | - Hongchun Qu
- College of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing, China
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhaoni Li
- College of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing, China
| | - Lin Li
- College of Computer Science and Technology, Chongqing University of Post and Telecommunications, Chongqing, China
| | - Xiaoming Tang
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Fei Guo
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
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55
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Rezaee K, Jeon G, Khosravi MR, Attar HH, Sabzevari A. Deep learning‐based microarray cancer classification and ensemble gene selection approach. IET Syst Biol 2022; 16:120-131. [PMID: 35790076 PMCID: PMC9290776 DOI: 10.1049/syb2.12044] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/04/2022] [Accepted: 05/31/2022] [Indexed: 12/19/2022] Open
Abstract
Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k‐nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis‐related brain tissue lesions were examined to show the generalisability of the model method.
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Affiliation(s)
- Khosro Rezaee
- Department of Biomedical Engineering Meybod University Meybod Iran
| | - Gwanggil Jeon
- Department of Embedded Systems Engineering College of Information Technology Incheon National University Incheon Korea
| | | | - Hani H. Attar
- Department of Energy Engineering Zarqa University Zarqa Jordan
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56
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Dokeroglu T, Deniz A, Kiziloz HE. A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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57
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Ding X, Yang F, Zhong Y, Cao J. A Novel Recursive Gene Selection Method Based on Least Square Kernel Extreme Learning Machine. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2026-2038. [PMID: 33764877 DOI: 10.1109/tcbb.2021.3068846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we propose a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM. The proposed method is called LSKELM-RFE which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of output weights of LSKELM and finally removes a "least important" gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.
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58
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Robust dual-graph regularized and minimum redundancy based on self-representation for semi-supervised feature selection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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59
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Multi-classification for high-dimensional data using probabilistic neural networks. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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60
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Aguilera A, Pezoa R, Rodríguez-Delherbe A. A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00774-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractClassifying histopathology images on a pixel-level requires sets of features able to capture the complex characteristics of the images, like the irregular cell morphology and the color heterogeneity on the tissue aspect. In this context, feature selection becomes a crucial step in the classification process such that it reduces model complexity and computational costs, avoids overfitting, and thereby it improves the model performance. In this study, we propose a new ensemble feature selection method by combining a set of base selectors, classifiers, and rank aggregation methods, aiming to determine from any initial set of handcrafted features, a smaller set of relevant color and texture pixel-level features, subsequently used for segmenting HER2 overexpression on a pixel-level, in breast cancer tissue images. We have been able to significantly reduce the set of initial features, using the proposed ensemble feature selection method. The best results are obtained using $$\chi ^2$$
χ
2
, Random Forest, and Runoff as the based selector, classifier, and aggregation method, respectively. The classification performance of the best model trained on the selected features set results in 0.939 recall, 0.866 specificity, 0.903 accuracy, 0.875 precision, and 0.906 F1-score.
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61
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RL-SSI Model: Adapting a Supervised Learning Approach to a Semi-Supervised Approach for Human Action Recognition. ELECTRONICS 2022. [DOI: 10.3390/electronics11091471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Generally, the action recognition task requires a vast amount of labeled data, which represents a time-consuming human annotation effort. To mitigate the dependency on labeled data, this study proposes Semi-Supervised and Iterative Reinforcement Learning (RL-SSI), which adapts a supervised approach that uses 100% labeled data to a semi-supervised and iterative approach using reinforcement learning for human action recognition in videos. The JIGSAWS and Breakfast datasets were used to evaluate the RL-SSI model, because they are commonly used in the action segmentation task. The same applies to the performance metrics used in this work-F-Score (F1) and Edit Score-which are commonly applied for such tasks. In JIGSAWS tests, we observed that the RL-SSI outperformed previously developed state-of-the-art techniques in all quantitative measures, while using only 65% of the labeled data. When analysing the Breakfast tests, we compared the effectiveness of RL-SSI with the results of the self-supervised technique called SSTDA. We have found that RL-SSI outperformed SSTDA with an accuracy of 66.44% versus 65.8%, but RL-SSI was surpassed by the F1@10 segmentation measure, which presented an accuracy of 67.33% versus 69.3% for SSTDA. Despite this, our experiment only used 55.8% of the labeled data, while SSTDA used 65%. We conclude that our approach outperformed equivalent supervised learning methods and is comparable to SSTDA, when evaluated on multiple datasets of human action recognition, proving to be an important innovative method to successfully building solutions to reduce the amount of fully labeled data, leveraging the work of human specialists in the task of data labeling of videos, and their respectives frames, for human action recognition, thus reducing the required resources to accomplish it.
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62
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Nie F, Dong X, Tian L, Wang R, Li X. Unsupervised Feature Selection With Constrained ℓ₂,₀-Norm and Optimized Graph. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1702-1713. [PMID: 33361007 DOI: 10.1109/tnnls.2020.3043362] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this article, we propose a novel feature selection approach, named unsupervised feature selection with constrained l2,0 -norm (row-sparsity constrained) and optimized graph (RSOGFS), which unifies feature selection and similarity matrix construction into a general framework instead of independently performing the two-stage process; thus, the similarity matrix preserving the local manifold structure of data can be determined adaptively. Unlike those sparse learning-based feature selection methods that can only solve the relaxation or approximation problems by introducing sparsity regularization term into the objective function, the proposed method directly tackles the original l2,0 -norm constrained problem to achieve group feature selection. Two optimization strategies are provided to solve the original sparse constrained problem. The convergence and approximation guarantees for the new algorithms are rigorously proved, and the computational complexity and parameter determination are theoretically analyzed. Experimental results on real-world data sets show that the proposed method for solving a nonconvex problem is superior to the state of the arts for solving the relaxed or approximate convex problems.
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63
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Ding X, Yang F, Ma F. An Efficient Model Selection for Linear Discrimination Function-based Recursive Feature Elimination. J Biomed Inform 2022; 129:104070. [DOI: 10.1016/j.jbi.2022.104070] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/02/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
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64
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Using Feature Selection with Machine Learning for Generation of Insurance Insights. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063209] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Insurance is a data-rich sector, hosting large volumes of customer data that is analysed to evaluate risk. Machine learning techniques are increasingly used in the effective management of insurance risk. Insurance datasets by their nature, however, are often of poor quality with noisy subsets of data (or features). Choosing the right features of data is a significant pre-processing step in the creation of machine learning models. The inclusion of irrelevant and redundant features has been demonstrated to affect the performance of learning models. In this article, we propose a framework for improving predictive machine learning techniques in the insurance sector via the selection of relevant features. The experimental results, based on five publicly available real insurance datasets, show the importance of applying feature selection for the removal of noisy features before performing machine learning techniques, to allow the algorithm to focus on influential features. An additional business benefit is the revelation of the most and least important features in the datasets. These insights can prove useful for decision making and strategy development in areas/business problems that are not limited to the direct target of the downstream algorithms. In our experiments, machine learning techniques based on a set of selected features suggested by feature selection algorithms outperformed the full feature set for a set of real insurance datasets. Specifically, 20% and 50% of features in our five datasets had improved downstream clustering and classification performance when compared to whole datasets. This indicates the potential for feature selection in the insurance sector to both improve model performance and to highlight influential features for business insights.
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65
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Luo C, Zheng J, Li T, Chen H, Huang Y, Peng X. Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.068] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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66
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Tsagris M, Papadovasilakis Z, Lakiotaki K, Tsamardinos I. The γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1214-1224. [PMID: 33035156 DOI: 10.1109/tcbb.2020.3029952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Feature selection for predictive analytics is the problem of identifying a minimal-size subset of features that is maximally predictive of an outcome of interest. To apply to molecular data, feature selection algorithms need to be scalable to tens of thousands of features. In this paper, we propose γ-OMP, a generalisation of the highly-scalable Orthogonal Matching Pursuit feature selection algorithm. γ-OMP can handle (a)various types of outcomes, such as continuous, binary, nominal, time-to-event, (b)discrete (categorical)features, (c)different statistical-based stopping criteria, (d)several predictive models (e.g., linear or logistic regression), (e)various types of residuals, and (f)different types of association. We compare γ-OMP against LASSO, a prototypical, widely used algorithm for high-dimensional data. On both simulated data and several real gene expression datasets, γ-OMP is on par, or outperforms LASSO in binary classification (case-control data), regression (quantified outcomes), and time-to-event data (censored survival times). γ-OMP is based on simple statistical ideas, it is easy to implement and to extend, and our extensive evaluation shows that it is also effective in bioinformatics analysis settings.
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67
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Zhang R, Zhang Y, Li X. Unsupervised Feature Selection via Adaptive Graph Learning and Constraint. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1355-1362. [PMID: 33361001 DOI: 10.1109/tnnls.2020.3042330] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The performance of graph-based feature selection methods relies heavily on the quality of the construction of the similarity matrix. However, most of the graphs on these methods are initially fixed, where few of them are constrained. Once the graph is determined, it will remain constant in the whole optimization process. In other words, in case that the graph constructed on the raw data is not appropriate, it will drag down the entire algorithm. Aiming to tackle this defect, a novel unsupervised feature selection via adaptive graph learning and constraint (EGCFS) is proposed to select the uncorrelated yet discriminative features by exploiting the embedded graph learning and constraint. The adaptive graph learning method incorporates the structure of the similarity matrix into the optimization process, which not only learns the graph structure adaptively but also obtains the closed-form solution of the graph coefficient. Special graph constraint is embedded with the feature selection process to connect nearer data points with larger probability. The idea of maximizing between-class scatter matrix and the adaptive graph structure is integrated into a uniform framework to obtain excellent structural performance. Moreover, the proposed embedded graph constraint not only performs with manifold structure but also validates the link between graph-based approach and k -means from a unique perspective. Experiments on several benchmark data sets verify the effectiveness and superiority of the proposed method.
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68
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Mirzal A. Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1173-1192. [PMID: 32956065 DOI: 10.1109/tcbb.2020.3025486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM)are some of the most used methods. However, there is still a limited number of works that utilize statistical analysis to measure the significances of performance differences between these methods. In this paper, statistical analysis of performance differences between ten NMF, six spectral clustering, four GMM, and the standard kmeans algorithms in clustering eleven publicly available microarray gene expression datasets with the number of clusters ranges from two to ten is presented. The experimental results show that statistically NMFs and kmeans have similar performances and outperform spectral clustering. As spectral clustering can be used to uncover hidden manifold structures, the underperformance of spectral methods leads us to question whether the datasets have manifold structures. Visual inspection using multidimensional scaling plots indicates that such structures do not exist. Moreover, as the plots indicate that clusters in some datasets have elliptical boundaries, GMM methods are also utilized. The experimental results show that GMM methods outperform the other methods to some degree, and thus imply that the datasets follow gaussian distributions.
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69
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Feltes BC, Vieira IA, Parraga-Alava J, Meza J, Portmann E, Terán L, Dorn M. Feature selection reveal peripheral blood parameter's changes between COVID-19 infections patients from Brazil and Ecuador. INFECTION, GENETICS AND EVOLUTION 2022; 98:105228. [PMID: 35104680 PMCID: PMC8800568 DOI: 10.1016/j.meegid.2022.105228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 01/23/2022] [Indexed: 11/29/2022]
Abstract
The investigation of conventional complete blood-count (CBC) data for classifying the SARS-CoV-2 infection status became a topic of interest, particularly as a complementary laboratory tool in developing and third-world countries that financially struggled to test their population. Although hematological parameters in COVID-19-affected individuals from Asian and USA populations are available, there are no descriptions of comparative analyses of CBC findings between COVID-19 positive and negative cases from Latin American countries. In this sense, machine learning techniques have been employed to examine CBC data and aid in screening patients suspected of SARS-CoV-2 infection. In this work, we used machine learning to compare CBC data between two highly genetically distinguished Latin American countries: Brazil and Ecuador. We notice a clear distribution pattern of positive and negative cases between the two countries. Interestingly, almost all red blood cell count parameters were divergent. For males, neutrophils and lymphocytes are distinct between Brazil and Ecuador, while eosinophils are distinguished for females. Finally, neutrophils, lymphocytes, and monocytes displayed a particular distribution for both genders. Therefore, our findings demonstrate that the same set of CBC features relevant to one population is unlikely to apply to another. This is the first study to compare CBC data from two genetically distinct Latin American countries.
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Affiliation(s)
- Bruno César Feltes
- Department of Genetics, Institute of Bioscience, and Department of Biophysics, Institute of Bioscience, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Igor Araújo Vieira
- Genomic Medicine Laboratory, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
| | - Jorge Parraga-Alava
- Facultad de Ciencias Informaticas, Universidad Técnica de Manabí, Portoviejo, Manabí, Ecuador
| | - Jaime Meza
- Facultad de Ciencias Informaticas, Universidad Técnica de Manabí, Portoviejo, Manabí, Ecuador
| | - Edy Portmann
- Human-IST Institute, University of Fribourg, Fribourg, Switzerland
| | - Luis Terán
- Human-IST Institute, University of Fribourg, Fribourg, Switzerland
| | - Márcio Dorn
- Institute of Informatics, Center of Biotechnology, Federal University of Rio Grande do Sul, RS, Brazil; National Institute of Science and Technology - Forensic Science, Porto Alegre, RS, Brazil.
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70
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Heydari T, A. Langley M, Fisher CL, Aguilar-Hidalgo D, Shukla S, Yachie-Kinoshita A, Hughes M, M. McNagny K, Zandstra PW. IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data. PLoS Comput Biol 2022; 18:e1009907. [PMID: 35213533 PMCID: PMC8906617 DOI: 10.1371/journal.pcbi.1009907] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 03/09/2022] [Accepted: 02/08/2022] [Indexed: 01/03/2023] Open
Abstract
The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems. Executable GRNs provide an important strategy to model complex intracellular dynamics in development and disease. Here we introduce IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from single cell sequencing data. IQCELL is an integrative platform that includes modules for gene selection, building logical GRNs, and simulating developmental trajectories under normal and perturbed conditions. We demonstrate the utility of IQCELL by reconstructing GRNs for early mouse T-cell and red blood cell development. We show that IQCELL can “automatically” infer the vast majority of gene interactions previously reported from decades of experimental research. IQCELL also provides users with a platform to simulate the developmental trajectories of cells. We show that dynamic simulations of the inferred GRNs resemble experimentally observed gene expression dynamics and capture the effects of genetic perturbation studies. IQCELL offers a versatile tool to infer and simulate GRNs in dynamic biological systems.
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Affiliation(s)
- Tiam Heydari
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthew A. Langley
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Cynthia L. Fisher
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel Aguilar-Hidalgo
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shreya Shukla
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Notch Therapeutics, Vancouver, British Columbia, Canada
| | - Ayako Yachie-Kinoshita
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Michael Hughes
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Kelly M. McNagny
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Peter W. Zandstra
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
- * E-mail:
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71
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Davahli A, Shamsi M, Abaei G, Khosravi A. Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2021.1960627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Azam Davahli
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Mahboubeh Shamsi
- Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran
| | - Golnoush Abaei
- Department of Software Engineering, School of Information Technology, Monash University Malaysia, Malaysia
| | - Arash Khosravi
- Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
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72
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Guo Z, Chen M, Fan Y, Song Y. A general adaptive ridge regression method for generalized linear models: an iterative re-weighting approach. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2028841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Zijun Guo
- College of Science, University of Shanghai for Science and Technology, Shanghai, China
| | - Mengxing Chen
- College of Science, University of Shanghai for Science and Technology, Shanghai, China
| | - Yali Fan
- College of Science, University of Shanghai for Science and Technology, Shanghai, China
| | - Yan Song
- Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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73
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Mehrpooya A, Saberi-Movahed F, Azizizadeh N, Rezaei-Ravari M, Saberi-Movahed F, Eftekhari M, Tavassoly I. High dimensionality reduction by matrix factorization for systems pharmacology. Brief Bioinform 2022; 23:bbab410. [PMID: 34891155 PMCID: PMC8898012 DOI: 10.1093/bib/bbab410] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/20/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022] Open
Abstract
The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.
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Affiliation(s)
- Adel Mehrpooya
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farid Saberi-Movahed
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Najmeh Azizizadeh
- Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Iran
| | - Mohammad Rezaei-Ravari
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | | | - Mahdi Eftekhari
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA
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74
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Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets. ALGORITHMS 2022. [DOI: 10.3390/a15010021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Analysis of high-dimensional data, with more features (p) than observations (N) (p>N), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.
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75
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Multi-objective feature selection based on quasi-oppositional based Jaya algorithm for microarray data. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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76
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Alhenawi E, Al-Sayyed R, Hudaib A, Mirjalili S. Feature selection methods on gene expression microarray data for cancer classification: A systematic review. Comput Biol Med 2022; 140:105051. [PMID: 34839186 DOI: 10.1016/j.compbiomed.2021.105051] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/01/2021] [Accepted: 11/15/2021] [Indexed: 11/29/2022]
Abstract
This systematic review provides researchers interested in feature selection (FS) for processing microarray data with comprehensive information about the main research directions for gene expression classification conducted during the recent seven years. A set of 132 researches published by three different publishers is reviewed. The studied papers are categorized into nine directions based on their objectives. The FS directions that received various levels of attention were then summarized. The review revealed that 'propose hybrid FS methods' represented the most interesting research direction with a percentage of 34.9%, while the other directions have lower percentages that ranged from 13.6% down to 3%. This guides researchers to select the most competitive research direction. Papers in each category are thoroughly reviewed based on six perspectives, mainly: method(s), classifier(s), dataset(s), dataset dimension(s) range, performance metric(s), and result(s) achieved.
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Affiliation(s)
- Esra'a Alhenawi
- King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
| | - Rizik Al-Sayyed
- King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
| | - Amjad Hudaib
- King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
| | - Seyedali Mirjalili
- Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, 4006, QLD, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
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77
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Harbola A, Negi D, Manchanda M, Kesharwani RK. Bioinformatics and biological data mining. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00019-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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78
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Correa R, Alonso-Pupo N, Hernández Rodríguez EW. Multi-omics data integration approaches for precision oncology. Mol Omics 2022; 18:469-479. [DOI: 10.1039/d1mo00411e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...
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79
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A Two-Stage Method Based on Multiobjective Differential Evolution for Gene Selection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5227377. [PMID: 34966420 PMCID: PMC8712129 DOI: 10.1155/2021/5227377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/06/2021] [Accepted: 12/03/2021] [Indexed: 11/17/2022]
Abstract
Microarray gene expression data provide a prospective way to diagnose disease and classify cancer. However, in bioinformatics, the gene selection problem, i.e., how to select the most informative genes from thousands of genes, remains challenging. This problem is a specific feature selection problem with high-dimensional features and small sample sizes. In this paper, a two-stage method combining a filter feature selection method and a wrapper feature selection method is proposed to solve the gene selection problem. In contrast to common methods, the proposed method models the gene selection problem as a multiobjective optimization problem. Both stages employ the same multiobjective differential evolution (MODE) as the search strategy but incorporate different objective functions. The three objective functions of the filter method are mainly based on mutual information. The two objective functions of the wrapper method are the number of selected features and the classification error of a naive Bayes (NB) classifier. Finally, the performance of the proposed method is tested and analyzed on six benchmark gene expression datasets. The experimental results verified that this paper provides a novel and effective way to solve the gene selection problem by applying a multiobjective optimization algorithm.
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80
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Shu L, Huang K, Jiang W, Wu W, Liu H. Feature selection using autoencoders with Bayesian methods to high-dimensional data. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-211348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
It is easy to lead to poor generalization in machine learning tasks using real-world data directly, since such data is usually high-dimensional dimensionality and limited. Through learning the low dimensional representations of high-dimensional data, feature selection can retain useful features for machine learning tasks. Using these useful features effectively trains machine learning models. Hence, it is a challenge for feature selection from high-dimensional data. To address this issue, in this paper, a hybrid approach consisted of an autoencoder and Bayesian methods is proposed for a novel feature selection. Firstly, Bayesian methods are embedded in the proposed autoencoder as a special hidden layer. This of doing is to increase the precision during selecting non-redundant features. Then, the other hidden layers of the autoencoder are used for non-redundant feature selection. Finally, compared with the mainstream approaches for feature selection, the proposed method outperforms them. We find that the way consisted of autoencoders and probabilistic correction methods is more meaningful than that of stacking architectures or adding constraints to autoencoders as regards feature selection. We also demonstrate that stacked autoencoders are more suitable for large-scale feature selection, however, sparse autoencoders are beneficial for a smaller number of feature selection. We indicate that the value of the proposed method provides a theoretical reference to analyze the optimality of feature selection.
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Affiliation(s)
- Lei Shu
- Chongqing Aerospace Polytechnic, Chongqing, China
| | - Kun Huang
- Urban Vocational College of Sichuan, P.R. China
| | - Wenhao Jiang
- Chongqing Aerospace Polytechnic, Chongqing, China
| | - Wenming Wu
- Chongqing Aerospace Polytechnic, Chongqing, China
| | - Hongling Liu
- Chongqing Aerospace Polytechnic, Chongqing, China
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81
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Uzma, Halim Z. An ensemble filter-based heuristic approach for cancerous gene expression classification. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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82
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Sheng J, Li WV. Selecting gene features for unsupervised analysis of single-cell gene expression data. Brief Bioinform 2021; 22:bbab295. [PMID: 34351383 PMCID: PMC8574996 DOI: 10.1093/bib/bbab295] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/17/2021] [Accepted: 07/12/2021] [Indexed: 11/15/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented molecular resolution. In order to evaluate various biological hypotheses using high-dimensional single-cell gene expression data, most computational and statistical methods depend on a gene feature selection step to identify genes with high biological variability and reduce computational complexity. Even though many gene selection methods have been developed for scRNA-seq analysis, there lacks a systematic comparison of the assumptions, statistical models, and selection criteria used by these methods. In this article, we summarize and discuss 17 computational methods for selecting gene features in unsupervised analysis of single-cell gene expression data, with unified notations and statistical frameworks. Our discussion provides a useful summary to help practitioners select appropriate methods based on their assumptions and applicability, and to assist method developers in designing new computational tools for unsupervised learning of scRNA-seq data.
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Affiliation(s)
- Jie Sheng
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Wei Vivian Li
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ 08854, USA
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83
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84
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An efficient feature selection framework based on information theory for high dimensional data. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107729] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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85
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Atashgahi Z, Sokar G, van der Lee T, Mocanu E, Mocanu DC, Veldhuis R, Pechenizkiy M. Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. Mach Learn 2021. [DOI: 10.1007/s10994-021-06063-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractMajor complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which identifies the most relevant and informative attributes of a dataset, has been introduced as a solution to this problem. Most of the existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and energy resources. In this paper, a novel and flexible method for unsupervised feature selection is proposed. This method, named QuickSelection (The code is available at: https://github.com/zahraatashgahi/QuickSelection), introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance. This criterion, blended with sparsely connected denoising autoencoders trained with the sparse evolutionary training procedure, derives the importance of all input features simultaneously. We implement QuickSelection in a purely sparse manner as opposed to the typical approach of using a binary mask over connections to simulate sparsity. It results in a considerable speed increase and memory reduction. When tested on several benchmark datasets, including five low-dimensional and three high-dimensional datasets, the proposed method is able to achieve the best trade-off of classification and clustering accuracy, running time, and maximum memory usage, among widely used approaches for feature selection. Besides, our proposed method requires the least amount of energy among the state-of-the-art autoencoder-based feature selection methods.
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86
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Sun R, Hou X, Li X, Xie Y, Nie S. Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast-Enhanced MRI. J Magn Reson Imaging 2021; 55:1518-1534. [PMID: 34668601 DOI: 10.1002/jmri.27955] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/27/2021] [Accepted: 10/01/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Imaging-driven deep learning strategies focus on training from scratch and transfer learning. However, the performance of training from scratch is often impeded by the lack of large-scale labeled training data. Additionally, owing to the differences between source and target domains, analyzing medical image tasks satisfactorily via transfer learning based on ImageNet is difficult. PURPOSE To investigate two transfer learning algorithms for breast cancer molecular subtype prediction (luminal and non-luminal) based on unsupervised pre-training and ensemble learning: M_EL and B_EL, using malignant and benign datasets as the source domain, respectively. STUDY TYPE Retrospective. POPULATION Eight hundred and thirty-three female patients with histologically confirmed breast lesions (567 benign and 266 malignant cases) were selected. In the 5-fold cross-validation, the malignant cohort was randomly divided into 5 subsets to form a training set (80%) and a validation set (20%). FIELD STRENGTH/SEQUENCE 3.0 T, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using T1-weighted high-resolution isotropic volume examination. ASSESSMENT First, three datasets acquired at different times post-contrast were preprocessed as unlabeled source domains. Second, three baseline networks corresponding to the different MRI post-contrast phases were built, optimized by a combination of mutual information maximization between high- and low-level representations and prior distribution constraints. Next, the pre-trained networks were fine-tuned on the labeled target domain. Finally, prediction results were integrated using weighted voting-based ensemble learning. STATISTICAL TESTS Mean accuracy, precision, specificity, and area under receiver operating characteristic curve (AUC) were obtained with 5-fold cross-validation. P < 0.05 was considered to be statistically significant. RESULTS Compared with a convolutional long short-term memory network, pre-trained VGG-16, VGG-19, and DenseNet-121 from ImageNet, M_EL and B_EL exhibited significantly more optimized prediction performance (specificity: 90.5% and 89.9%; accuracy: 82.6% and 81.1%; precision: 91.2% and 90.9%; AUC: 0.836 and 0.823, respectively). DATA CONCLUSION Transfer learning based on unsupervised pre-training may facilitate automatic prediction of breast cancer molecular subtypes. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Rong Sun
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xuewen Hou
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiujuan Li
- Medical Imaging Center, Tai'an Central Hospital, Tai'an, China
| | - Yuanzhong Xie
- Medical Imaging Center, Tai'an Central Hospital, Tai'an, China
| | - Shengdong Nie
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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87
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Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6079163. [PMID: 34646886 PMCID: PMC8505067 DOI: 10.1155/2021/6079163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/10/2020] [Accepted: 07/18/2020] [Indexed: 11/17/2022]
Abstract
Over recent years, feature selection (FS) has gained more attention in intelligent diagnosis. This study is aimed at evaluating FS methods in a unified framework for mammographic breast cancer diagnosis. After FS methods generated rank lists according to feature importance, the framework added features incrementally as the input of random forest which performed as the classifier for breast lesion classification. In this study, 10 FS methods were evaluated and the digital database for screening mammography (1104 benign and 980 malignant lesions) was analyzed. The classification performance was quantified with the area under the curve (AUC), and accuracy, sensitivity, and specificity were also considered. Experimental results suggested that both infinite latent FS method (AUC, 0.866 ± 0.028) and RELIEFF (AUC, 0.855 ± 0.020) achieved good prediction (AUC ≥ 0.85) when 6 features were used, followed by correlation-based FS method (AUC, 0.867 ± 0.023) using 7 features and WILCOXON (AUC, 0.887 ± 0.019) using 8 features. The reliability of the diagnosis models was also verified, indicating that correlation-based FS method was generally superior over other methods. Identification of discriminative features among high-throughput ones remains an unavoidable challenge in intelligent diagnosis, and extra efforts should be made toward accurate and efficient feature selection.
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88
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Solorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF. A survey on feature selection methods for mixed data. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10072-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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89
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A multi-leader Harris hawk optimization based on differential evolution for feature selection and prediction influenza viruses H1N1. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10075-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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90
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Ahmed H, Alarabi L, El-Sappagh S, Soliman H, Elmogy M. Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles. PeerJ Comput Sci 2021; 7:e697. [PMID: 34616886 PMCID: PMC8459785 DOI: 10.7717/peerj-cs.697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. METHODS We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. RESULTS It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. CONCLUSIONS Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.
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Affiliation(s)
- Hala Ahmed
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Louai Alarabi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Hassan Soliman
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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91
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Bose S, Das C, Banerjee A, Ghosh K, Chattopadhyay M, Chattopadhyay S, Barik A. An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples. PeerJ Comput Sci 2021; 7:e671. [PMID: 34616883 PMCID: PMC8459790 DOI: 10.7717/peerj-cs.671] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 07/20/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis. METHODS In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets. RESULTS To assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.
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Affiliation(s)
- Shilpi Bose
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India
| | - Chandra Das
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India
| | - Abhik Banerjee
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India
| | - Kuntal Ghosh
- Machine Intelligence Unit & Center for Soft Computing Research, Indian Statistical Institute, Kolkata, West Bengal, India
| | | | - Samiran Chattopadhyay
- Department of Information Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Aishwarya Barik
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India
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92
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Saravanan V, Manikandan R, Maharasan KS, Ramesh R. Optimized Attribute Selection Using Artificial Plant (AP) Algorithm with ESVM Classifier (AP-ESVM) and Improved Singular Value Decomposition (ISVD)-Based Dimensionality Reduction for Large Micro-array Biological Data. Interdiscip Sci 2021; 13:463-475. [PMID: 32533456 DOI: 10.1007/s12539-020-00377-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/23/2020] [Accepted: 05/30/2020] [Indexed: 06/11/2023]
Abstract
In the tremendous field of the bioinformatics look into, enormous volume of genetic information has been produced. Higher throughput gadgets are made accessible at lower cost made the age of Big data. In a time of developing information multifaceted nature and volume and the approach of huge information, feature selection has a key task to carry out in decreasing high dimensionality in AI issues. Dealing with such huge data has turned out to be incredibly testing strategy for choosing the exact features in enormous medical databases. Large clinical data frequently comprise of an enormous number of identifiers of the disease. Data mining when applied to clinical data for identification of diseases, a few identifiers are will not be much useful and sometimes may even have negative impacts. Consequently, when the FS is applied, it is vital as it can expel those insignificant disease identifiers. It likewise builds the adequacy of decision by a physician emotionally supportive network by viably diminishing the time of learning of the framework. In this paper, a unique approach is presented for the feature selection utilizing the Artificial Plant algorithm which uses the Enhanced Support Vector Machine classifier. The features got are additionally dimensionally decreased by presenting the Improved Singular Value Decomposition strategy; finally, enhancement is done by the outstanding BAT streamlining method. The examinations are completed with real-time large cervical cancer data and it demonstrated to be more effective than the current methods.
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Affiliation(s)
- V Saravanan
- Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India.
| | - R Manikandan
- All India Council for Technical Education, Thiruvananthapuram, India
| | - K S Maharasan
- True Friend Management Support Service Pvt Ltd., Chennai, India
| | - R Ramesh
- Sri Krishna Arts and Science College, Coimbatore, India
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93
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Rana P, Thai P, Dinh T, Ghosh P. Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection. Cancers (Basel) 2021; 13:cancers13174297. [PMID: 34503106 PMCID: PMC8428340 DOI: 10.3390/cancers13174297] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Here we introduce a new feature selection algorithm DTA, which selects important, non-redundant, and relevant features from diverse omics data. DTA selects non-redundant features by maximizing the similarity between each patient pair by an approximate k-cover algorithm. We successfully applied this algorithm to three different biological problems: (a) disease to healthy sample classification, (b) multiclass classification of different disease samples, and (c) disease subtypes detection. DTA outperformed other feature selection techniques in the binary classification of healthy and disease samples and multiclass classification of various diseases. It also improved the performance of a subtype detection algorithm by selecting the important features for few cancer types. Abstract Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.
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94
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Dorn M, Grisci BI, Narloch PH, Feltes BC, Avila E, Kahmann A, Alho CS. Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets. PeerJ Comput Sci 2021; 7:e670. [PMID: 34458574 PMCID: PMC8372002 DOI: 10.7717/peerj-cs.670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/20/2021] [Indexed: 06/13/2023]
Abstract
The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.
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Affiliation(s)
- Marcio Dorn
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- Center of Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- Forensic Science, National Institute of Science and Technology, Porto Alegre, RS, Brazil
| | - Bruno Iochins Grisci
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Pedro Henrique Narloch
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Bruno César Feltes
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Eduardo Avila
- Forensic Science, National Institute of Science and Technology, Porto Alegre, RS, Brazil
- School of Health and Life Sciences, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Alessandro Kahmann
- Institute of Mathematics, Statistics and Physics, Federal University of Rio Grande, Rio Grande, RS, Brazil
| | - Clarice Sampaio Alho
- Forensic Science, National Institute of Science and Technology, Porto Alegre, RS, Brazil
- School of Health and Life Sciences, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil
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95
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Cheng LH, Hsu TC, Lin C. Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction. Sci Rep 2021; 11:14914. [PMID: 34290286 PMCID: PMC8295302 DOI: 10.1038/s41598-021-92864-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.
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Affiliation(s)
- Li-Hsin Cheng
- grid.38348.340000 0004 0532 0580Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013 Taiwan
| | - Te-Cheng Hsu
- grid.38348.340000 0004 0532 0580Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013 Taiwan
| | - Che Lin
- grid.19188.390000 0004 0546 0241Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, Taipei, 10617 Taiwan
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96
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Madhu P.K. R, Subbaiah J, Krithivasan K. RF‐LSTM‐based method for prediction and diagnosis of fouling in heat exchanger. ASIA-PAC J CHEM ENG 2021. [DOI: 10.1002/apj.2684] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Resma Madhu P.K.
- School of Electrical and Electronics Engineering SASTRA Deemed University Thanjavur India
| | - Jayalalitha Subbaiah
- School of Electrical and Electronics Engineering SASTRA Deemed University Thanjavur India
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97
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Meng Y, Jin M. HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis. Front Cell Dev Biol 2021; 9:696359. [PMID: 34277640 PMCID: PMC8278475 DOI: 10.3389/fcell.2021.696359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/19/2021] [Indexed: 11/15/2022] Open
Abstract
The emergence of high-throughput RNA-seq data has offered unprecedented opportunities for cancer diagnosis. However, capturing biological data with highly nonlinear and complex associations by most existing approaches for cancer diagnosis has been challenging. In this study, we propose a novel hierarchical feature selection and second learning probability error ensemble model (named HFS-SLPEE) for precision cancer diagnosis. Specifically, we first integrated protein-coding gene expression profiles, non-coding RNA expression profiles, and DNA methylation data to provide rich information; afterward, we designed a novel hierarchical feature selection method, which takes the CpG-gene biological associations into account and can select a compact set of superior features; next, we used four individual classifiers with significant differences and apparent complementary to build the heterogeneous classifiers; lastly, we developed a second learning probability error ensemble model called SLPEE to thoroughly learn the new data consisting of classifiers-predicted class probability values and the actual label, further realizing the self-correction of the diagnosis errors. Benchmarking comparisons on TCGA showed that HFS-SLPEE performs better than the state-of-the-art approaches. Moreover, we analyzed in-depth 10 groups of selected features and found several novel HFS-SLPEE-predicted epigenomics and epigenetics biomarkers for breast invasive carcinoma (BRCA) (e.g., TSLP and ADAMTS9-AS2), lung adenocarcinoma (LUAD) (e.g., HBA1 and CTB-43E15.1), and kidney renal clear cell carcinoma (KIRC) (e.g., IRX2 and BMPR1B-AS1).
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Affiliation(s)
| | - Min Jin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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98
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Relevance aggregation for neural networks interpretability and knowledge discovery on tabular data. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.052] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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99
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Jin B, Fu C, Jin Y, Yang W, Li S, Zhang G, Wang Z. An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification. SENSORS 2021; 21:s21113627. [PMID: 34071066 PMCID: PMC8197094 DOI: 10.3390/s21113627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 11/29/2022]
Abstract
Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.
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Affiliation(s)
- Bo Jin
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (B.J.); (Y.J.); (S.L.); (G.Z.); (Z.W.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;
| | - Chunling Fu
- School of Physics and Electronics, Henan University, Kaifeng 475004, China
- Correspondence:
| | - Yong Jin
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (B.J.); (Y.J.); (S.L.); (G.Z.); (Z.W.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;
| | - Wei Yang
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;
| | - Shengbin Li
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (B.J.); (Y.J.); (S.L.); (G.Z.); (Z.W.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;
| | - Guangyao Zhang
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (B.J.); (Y.J.); (S.L.); (G.Z.); (Z.W.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;
| | - Zheng Wang
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (B.J.); (Y.J.); (S.L.); (G.Z.); (Z.W.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;
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100
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Wang Y, Wang J, Che H. Two-timescale neurodynamic approaches to supervised feature selection based on alternative problem formulations. Neural Netw 2021; 142:180-191. [PMID: 34020085 DOI: 10.1016/j.neunet.2021.04.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/21/2021] [Accepted: 04/29/2021] [Indexed: 10/21/2022]
Abstract
Feature selection is a crucial step in data processing and machine learning. While many greedy and sequential feature selection approaches are available, a holistic neurodynamics approach to supervised feature selection is recently developed via fractional programming by minimizing feature redundancy and maximizing relevance simultaneously. In view that the gradient of the fractional objective function is also fractional, alternative problem formulations are desirable to obviate the fractional complexity. In this paper, the fractional programming problem formulation is equivalently reformulated as bilevel and bilinear programming problems without using any fractional function. Two two-timescale projection neural networks are adapted for solving the reformulated problems. Experimental results on six benchmark datasets are elaborated to demonstrate the global convergence and high classification performance of the proposed neurodynamic approaches in comparison with six mainstream feature selection approaches.
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Affiliation(s)
- Yadi Wang
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China; Institute of Data and Knowledge Engineering, School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.
| | - Jun Wang
- Department of Computer Science and School of Data Science, City University of Hong Kong, Kowloon, Hong Kong; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong, China.
| | - Hangjun Che
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China.
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