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For: Liang S, Ma A, Yang S, Wang Y, Ma Q. A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis. Comput Struct Biotechnol J 2018;16:88-97. [PMID: 30275937 DOI: 10.1016/j.csbj.2018.02.005] [Cited by in Crossref: 31] [Cited by in F6Publishing: 18] [Article Influence: 7.8] [Reference Citation Analysis]
Number Citing Articles
1 Agrawal S, Singh Sisodia D, Kumar Nagwani N. Functional characterization of unknown protein sequences using Neuro-Fuzzy based machine learning approach and sequence augmented feature. Expert Systems with Applications 2022;205:117760. [DOI: 10.1016/j.eswa.2022.117760] [Reference Citation Analysis]
2 Gerolami J, Wong JJM, Zhang R, Chen T, Imtiaz T, Smith M, Jamaspishvili T, Koti M, Glasgow JI, Mousavi P, Renwick N, Tyryshkin K. A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data. Diagnostics 2022;12:1997. [DOI: 10.3390/diagnostics12081997] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Sarkar A, Singh A, Bailey R, Dobra A, Kahveci T. Optimal separation of high dimensional transcriptome for complex multigenic traits. Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics 2022. [DOI: 10.1145/3535508.3545506] [Reference Citation Analysis]
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5 Geetha P, Umamaheswari S. Choose most efficient features of breast cancer using an SVM classifier for breast cancer diagnosis. 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) 2022. [DOI: 10.1109/accai53970.2022.9752597] [Reference Citation Analysis]
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7 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 2021;140:105051. [PMID: 34839186 DOI: 10.1016/j.compbiomed.2021.105051] [Cited by in Crossref: 13] [Cited by in F6Publishing: 17] [Article Influence: 13.0] [Reference Citation Analysis]
8 Solorio-fernández S, Carrasco-ochoa JA, Martínez-trinidad JF. A survey on feature selection methods for mixed data. Artif Intell Rev. [DOI: 10.1007/s10462-021-10072-6] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
9 Agrawal S, Sisodia DS, Nagwani NK. Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences. Med Biol Eng Comput 2021;59:2297-310. [PMID: 34545514 DOI: 10.1007/s11517-021-02436-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Chao W, Quan Z. A Machine Learning Method for Differentiating and Predicting Human‐Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein. Chin j electron 2021;30:815-23. [DOI: 10.1049/cje.2021.06.003] [Reference Citation Analysis]
11 Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F, Cereda M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int J Mol Sci 2021;22:4563. [PMID: 33925407 DOI: 10.3390/ijms22094563] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
12 Chatzilygeroudis KI, Vrahatis AG, Tasoulis SK, Vrahatis MN. Feature Selection in Single-Cell RNA-seq Data via a Genetic Algorithm. Lecture Notes in Computer Science 2021. [DOI: 10.1007/978-3-030-92121-7_6] [Reference Citation Analysis]
13 Barona-lopez LI, Valdivieso-caraguay AL, Benalcazar ME, Aguas X, Zea JA. Feature Evaluation of EMG Signals for Hand Gesture Recognition Based on Mutual Information, Fuzzy Entropy and RES Index. Advances and Applications in Computer Science, Electronics and Industrial Engineering 2021. [DOI: 10.1007/978-981-33-4565-2_7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Wang C, Wu J, Xu L, Zou Q. NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data. Microb Genom 2020;6. [PMID: 33245691 DOI: 10.1099/mgen.0.000483] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 Wang X, Gao P, Liu Y, Li H, Lu F. Predicting Thermophilic Proteins by Machine Learning. CBIO 2020;15:493-502. [DOI: 10.2174/1574893615666200207094357] [Cited by in Crossref: 80] [Cited by in F6Publishing: 83] [Article Influence: 40.0] [Reference Citation Analysis]
16 Nair TM. Building and Interpreting Artificial Neural Network Models for Biological Systems. Methods Mol Biol 2021;2190:185-94. [PMID: 32804366 DOI: 10.1007/978-1-0716-0826-5_8] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
17 Vijayasarveswari V, Andrew AM, Jusoh M, Sabapathy T, Raof RAA, Yasin MNM, Ahmad RB, Khatun S, Rahim HA. Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction. PLoS One 2020;15:e0229367. [PMID: 32790672 DOI: 10.1371/journal.pone.0229367] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
18 Cai J, Wang D, Chen R, Niu Y, Ye X, Su R, Xiao G, Wei L. A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol. Front Bioeng Biotechnol 2020;8:502. [PMID: 32582654 DOI: 10.3389/fbioe.2020.00502] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
19 Hambali MA, Oladele TO, Adewole KS. Microarray cancer feature selection: Review, challenges and research directions. International Journal of Cognitive Computing in Engineering 2020;1:78-97. [DOI: 10.1016/j.ijcce.2020.11.001] [Cited by in Crossref: 11] [Cited by in F6Publishing: 14] [Article Influence: 5.5] [Reference Citation Analysis]
20 Zeng R, Liao M. Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications. Front Bioeng Biotechnol 2020;8:274. [PMID: 32373597 DOI: 10.3389/fbioe.2020.00274] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
21 Kalina J, Matonoha C. A sparse pair-preserving centroid-based supervised learning method for high-dimensional biomedical data or images. Biocybernetics and Biomedical Engineering 2020;40:774-86. [DOI: 10.1016/j.bbe.2020.03.008] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
22 Vijayasarveswari V, Andrew A, Jusoh M, Sabapathy T, Raof R, Yasin M, Ahmad R, Khatun S. Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction.. [DOI: 10.1101/2020.02.06.936831] [Reference Citation Analysis]
23 Hua Z, Zhou J, Hua Y, Zhang W. Strong approximate Markov blanket and its application on filter-based feature selection. Applied Soft Computing 2020;87:105957. [DOI: 10.1016/j.asoc.2019.105957] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
24 Li G, Gao X. The Feature Compression Algorithms for Identifying Cytokines Based on CNT Features. IEEE Access 2020;8:83645-83654. [DOI: 10.1109/access.2020.2989749] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
25 Polewko-klim A, Rudnicki WR. Analysis of Ensemble Feature Selection for Correlated High-Dimensional RNA-Seq Cancer Data. Lecture Notes in Computer Science 2020. [DOI: 10.1007/978-3-030-50420-5_39] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
26 Pirgazi J, Alimoradi M, Esmaeili Abharian T, Olyaee MH. An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets. Sci Rep 2019;9:18580. [PMID: 31819106 DOI: 10.1038/s41598-019-54987-1] [Cited by in Crossref: 31] [Cited by in F6Publishing: 32] [Article Influence: 10.3] [Reference Citation Analysis]
27 Liang S, Yang S, Liang D, Ma J, Tian Y, Zhao J, Zhang X, Xu Y, Wang Y. A novel matched-pairs feature selection method considering with tumor purity for differential gene expression analyses. Mathematical Biosciences 2019;311:39-48. [DOI: 10.1016/j.mbs.2019.02.007] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
28 Cherrington M, Airehrour D, Lu J, Xu Q, Wade S, Madanian S. Feature Selection Methods for Linked Data: Limitations, Capabilities and Potentials. Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '19 2019. [DOI: 10.1145/3365109.3368792] [Cited by in Crossref: 2] [Article Influence: 0.7] [Reference Citation Analysis]