262 related articles for article (PubMed ID: 31138112)
1. Robust identification of molecular phenotypes using semi-supervised learning.
Roder H; Oliveira C; Net L; Linstid B; Tsypin M; Roder J
BMC Bioinformatics; 2019 May; 20(1):273. PubMed ID: 31138112
[TBL] [Abstract][Full Text] [Related]
2. Weakly Semi-supervised phenotyping using Electronic Health records.
Nogues IE; Wen J; Lin Y; Liu M; Tedeschi SK; Geva A; Cai T; Hong C
J Biomed Inform; 2022 Oct; 134():104175. PubMed ID: 36064111
[TBL] [Abstract][Full Text] [Related]
3. Prognostic outcome prediction by semi-supervised least squares classification.
Shi M; Sheng Z; Tang H
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33094318
[TBL] [Abstract][Full Text] [Related]
4. Iterative processes: a review of semi-supervised machine learning in rehabilitation science.
Kringle EA; Knutson EC; Engstrom C; Terhorst L
Disabil Rehabil Assist Technol; 2020 Jul; 15(5):515-520. PubMed ID: 31282778
[No Abstract] [Full Text] [Related]
5. Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.
Liang Y; Chai H; Liu XY; Xu ZB; Zhang H; Leung KS
BMC Med Genomics; 2016 Mar; 9():11. PubMed ID: 26932592
[TBL] [Abstract][Full Text] [Related]
6. Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.
Yao C; Zhu X; Weigel KA
Genet Sel Evol; 2016 Nov; 48(1):84. PubMed ID: 27821057
[TBL] [Abstract][Full Text] [Related]
7. An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.
Stanescu A; Caragea D
BMC Syst Biol; 2015; 9 Suppl 5(Suppl 5):S1. PubMed ID: 26356316
[TBL] [Abstract][Full Text] [Related]
8. Classification of gene expression data: A hubness-aware semi-supervised approach.
Buza K
Comput Methods Programs Biomed; 2016 Apr; 127():105-13. PubMed ID: 27000293
[TBL] [Abstract][Full Text] [Related]
9. A semi-supervised machine learning framework for microRNA classification.
Sheikh Hassani M; Green JR
Hum Genomics; 2019 Oct; 13(Suppl 1):43. PubMed ID: 31639051
[TBL] [Abstract][Full Text] [Related]
10. A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.
Xiao Y; Wu J; Lin Z; Zhao X
Comput Methods Programs Biomed; 2018 Nov; 166():99-105. PubMed ID: 30415723
[TBL] [Abstract][Full Text] [Related]
11. MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.
Kim S; Lin CW; Tseng GC
Bioinformatics; 2016 Jul; 32(13):1966-73. PubMed ID: 27153719
[TBL] [Abstract][Full Text] [Related]
12. Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions.
Samadi ME; Mirzaieazar H; Mitsos A; Schuppert A
BMC Bioinformatics; 2024 Apr; 25(1):155. PubMed ID: 38641616
[TBL] [Abstract][Full Text] [Related]
13. Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration.
Siirtola P; Röning J
Sensors (Basel); 2019 Nov; 19(23):. PubMed ID: 31775243
[TBL] [Abstract][Full Text] [Related]
14. An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.
Bose S; Das C; Banerjee A; Ghosh K; Chattopadhyay M; Chattopadhyay S; Barik A
PeerJ Comput Sci; 2021; 7():e671. PubMed ID: 34616883
[TBL] [Abstract][Full Text] [Related]
15. Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping.
Orozco-Duque A; Bustamante J; Castellanos-Dominguez G
Biomed Eng Online; 2016 Apr; 15():44. PubMed ID: 27117088
[TBL] [Abstract][Full Text] [Related]
16. Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.
Smith AM; Walsh JR; Long J; Davis CB; Henstock P; Hodge MR; Maciejewski M; Mu XJ; Ra S; Zhao S; Ziemek D; Fisher CK
BMC Bioinformatics; 2020 Mar; 21(1):119. PubMed ID: 32197580
[TBL] [Abstract][Full Text] [Related]
17. Semi-Supervised Domain Adaptive Structure Learning.
Qin C; Wang L; Ma Q; Yin Y; Wang H; Fu Y
IEEE Trans Image Process; 2022; 31():7179-7190. PubMed ID: 36350853
[TBL] [Abstract][Full Text] [Related]
18. Semi-supervised learning for ordinal Kernel Discriminant Analysis.
Pérez-Ortiz M; Gutiérrez PA; Carbonero-Ruz M; Hervás-Martínez C
Neural Netw; 2016 Dec; 84():57-66. PubMed ID: 27639724
[TBL] [Abstract][Full Text] [Related]
19. A Semi-Automatic Annotation Approach for Human Activity Recognition.
Bota P; Silva J; Folgado D; Gamboa H
Sensors (Basel); 2019 Jan; 19(3):. PubMed ID: 30691040
[TBL] [Abstract][Full Text] [Related]
20. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.
Zhao X; Ning Q; Chai H; Ma Z
J Theor Biol; 2015 Jun; 374():60-5. PubMed ID: 25843215
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]