167 related articles for article (PubMed ID: 35484501)
1. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer.
Bhadra T; Mallik S; Hasan N; Zhao Z
BMC Bioinformatics; 2022 Apr; 23(Suppl 3):153. PubMed ID: 35484501
[TBL] [Abstract][Full Text] [Related]
2. Unsupervised Feature Selection Using an Integrated Strategy of Hierarchical Clustering With Singular Value Decomposition: An Integrative Biomarker Discovery Method With Application to Acute Myeloid Leukemia.
Bhadra T; Mallik S; Sohel A; Zhao Z
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(3):1354-1364. PubMed ID: 34495838
[TBL] [Abstract][Full Text] [Related]
3. Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data.
El-Manzalawy Y; Hsieh TY; Shivakumar M; Kim D; Honavar V
BMC Med Genomics; 2018 Sep; 11(Suppl 3):71. PubMed ID: 30255801
[TBL] [Abstract][Full Text] [Related]
4. Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.
Li F; Zhao C; Xia Z; Wang Y; Zhou X; Li GZ
BMC Complement Altern Med; 2012 Aug; 12():127. PubMed ID: 22898352
[TBL] [Abstract][Full Text] [Related]
5. Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.
Martin-Hernandez R; Espeso-Gil S; Domingo C; Latorre P; Hervas S; Hernandez Mora JR; Kotelnikova E
Front Mol Biosci; 2023; 10():1258902. PubMed ID: 38028548
[No Abstract] [Full Text] [Related]
6. Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction.
Shi P; Ray S; Zhu Q; Kon MA
BMC Bioinformatics; 2011 Sep; 12():375. PubMed ID: 21939564
[TBL] [Abstract][Full Text] [Related]
7. Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios.
Cascianelli S; Galzerano A; Masseroli M
J Biomed Inform; 2023 Aug; 144():104457. PubMed ID: 37488024
[TBL] [Abstract][Full Text] [Related]
8. Benchmark study of feature selection strategies for multi-omics data.
Li Y; Mansmann U; Du S; Hornung R
BMC Bioinformatics; 2022 Oct; 23(1):412. PubMed ID: 36199022
[TBL] [Abstract][Full Text] [Related]
9. Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.
Kishore A; Venkataramana L; Prasad DVV; Mohan A; Jha B
Med Biol Eng Comput; 2023 Nov; 61(11):2895-2919. PubMed ID: 37530887
[TBL] [Abstract][Full Text] [Related]
10. Two-stage feature selection for classification of gene expression data based on an improved Salp Swarm Algorithm.
Qin X; Zhang S; Yin D; Chen D; Dong X
Math Biosci Eng; 2022 Sep; 19(12):13747-13781. PubMed ID: 36654066
[TBL] [Abstract][Full Text] [Related]
11. Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.
Ang JC; Mirzal A; Haron H; Hamed HN
IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(5):971-989. PubMed ID: 26390495
[TBL] [Abstract][Full Text] [Related]
12. Feature set optimization in biomarker discovery from genome-scale data.
Fortino V; Scala G; Greco D
Bioinformatics; 2020 Jun; 36(11):3393-3400. PubMed ID: 32119073
[TBL] [Abstract][Full Text] [Related]
13. TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection.
Wang H; Zhang H; Dai Z; Chen MS; Yuan Z
BMC Med Genomics; 2013; 6 Suppl 1(Suppl 1):S3. PubMed ID: 23445528
[TBL] [Abstract][Full Text] [Related]
14. An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.
Zhang Y; Deng Q; Liang W; Zou X
Biomed Res Int; 2018; 2018():7538204. PubMed ID: 30228989
[TBL] [Abstract][Full Text] [Related]
15. An entropy-based gene selection method for cancer classification using microarray data.
Liu X; Krishnan A; Mondry A
BMC Bioinformatics; 2005 Mar; 6():76. PubMed ID: 15790388
[TBL] [Abstract][Full Text] [Related]
16. 3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection.
Mallik S; Sarkar A; Nath S; Maulik U; Das S; Pati SK; Ghosh S; Zhao Z
Front Genet; 2023; 14():1095330. PubMed ID: 36865387
[TBL] [Abstract][Full Text] [Related]
17. Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.
Park S; Soh J; Lee H
BMC Bioinformatics; 2021 May; 22(1):269. PubMed ID: 34034645
[TBL] [Abstract][Full Text] [Related]
18. Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
Jalali-Najafabadi F; Stadler M; Dand N; Jadon D; Soomro M; Ho P; Marzo-Ortega H; Helliwell P; Korendowych E; Simpson MA; Packham J; Smith CH; Barker JN; McHugh N; Warren RB; Barton A; Bowes J; ;
Sci Rep; 2021 Dec; 11(1):23335. PubMed ID: 34857774
[TBL] [Abstract][Full Text] [Related]
19. Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study.
Afrash MR; Mirbagheri E; Mashoufi M; Kazemi-Arpanahi H
BMC Med Inform Decis Mak; 2023 Apr; 23(1):54. PubMed ID: 37024885
[TBL] [Abstract][Full Text] [Related]
20. Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis.
Park J; Lee JW; Park M
BioData Min; 2023 Jul; 16(1):18. PubMed ID: 37420304
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]