123 related articles for article (PubMed ID: 38903761)
21. Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model.
Uddin MG; Rahman A; Rosa Taghikhah F; Olbert AI
Water Res; 2024 May; 255():121499. PubMed ID: 38552494
[TBL] [Abstract][Full Text] [Related]
22. Comparison of normalization methods for the analysis of metagenomic gene abundance data.
Pereira MB; Wallroth M; Jonsson V; Kristiansson E
BMC Genomics; 2018 Apr; 19(1):274. PubMed ID: 29678163
[TBL] [Abstract][Full Text] [Related]
23. Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model.
Kigo SN; Omondi EO; Omolo BO
Sci Rep; 2023 Oct; 13(1):17315. PubMed ID: 37828360
[TBL] [Abstract][Full Text] [Related]
24. Boundaries for genotype, phenotype, and pedigree truncation in genomic evaluations in pigs.
Bussiman F; Chen CY; Holl J; Bermann M; Legarra A; Misztal I; Lourenco D
J Anim Sci; 2023 Jan; 101():. PubMed ID: 37584978
[TBL] [Abstract][Full Text] [Related]
25. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions.
Yang L; Chen J
Microbiome; 2022 Aug; 10(1):130. PubMed ID: 35986393
[TBL] [Abstract][Full Text] [Related]
26. GMStool: GWAS-based marker selection tool for genomic prediction from genomic data.
Jeong S; Kim JY; Kim N
Sci Rep; 2020 Nov; 10(1):19653. PubMed ID: 33184432
[TBL] [Abstract][Full Text] [Related]
27. Examining the practical limits of batch effect-correction algorithms: When should you care about batch effects?
Zhou L; Chi-Hau Sue A; Bin Goh WW
J Genet Genomics; 2019 Sep; 46(9):433-443. PubMed ID: 31611172
[TBL] [Abstract][Full Text] [Related]
28. Genomic prediction of pig growth traits based on machine learning.
Chen D; Wang SJ; Zhao ZJ; Ji X; Shen Q; Yu Y; Cui SD; Wang JG; Chen ZY; Wang JY; Guo ZY; Wu PX; Tang GQ
Yi Chuan; 2023 Oct; 45(10):922-932. PubMed ID: 37872114
[TBL] [Abstract][Full Text] [Related]
29. Connecting genotype to phenotype in the era of high-throughput sequencing.
Henry CS; Overbeek R; Xia F; Best AA; Glass E; Gilbert J; Larsen P; Edwards R; Disz T; Meyer F; Vonstein V; Dejongh M; Bartels D; Desai N; D'Souza M; Devoid S; Keegan KP; Olson R; Wilke A; Wilkening J; Stevens RL
Biochim Biophys Acta; 2011 Oct; 1810(10):967-77. PubMed ID: 21421023
[TBL] [Abstract][Full Text] [Related]
30. A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs.
Yu W; Wang X; Jiang X; Zhao R; Zhao S
Environ Sci Pollut Res Int; 2024 Jan; 31(1):262-279. PubMed ID: 38015396
[TBL] [Abstract][Full Text] [Related]
31. Binary Metabolic Phenotypes and Phenotype Diversity Metrics for the Functional Characterization of Microbial Communities.
Iablokov SN; Novichkov PS; Osterman AL; Rodionov DA
Front Microbiol; 2021; 12():653314. PubMed ID: 34113324
[TBL] [Abstract][Full Text] [Related]
32. deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors.
Zou B; Zhang T; Zhou R; Jiang X; Yang H; Jin X; Bai Y
Front Genet; 2021; 12():708981. PubMed ID: 34447413
[TBL] [Abstract][Full Text] [Related]
33. FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms.
Mowlaei ME; Shi X
Genes (Basel); 2023 May; 14(5):. PubMed ID: 37239419
[TBL] [Abstract][Full Text] [Related]
34. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.
Ayoobi N; Sharifrazi D; Alizadehsani R; Shoeibi A; Gorriz JM; Moosaei H; Khosravi A; Nahavandi S; Gholamzadeh Chofreh A; Goni FA; Klemeš JJ; Mosavi A
Results Phys; 2021 Aug; 27():104495. PubMed ID: 34221854
[TBL] [Abstract][Full Text] [Related]
35. Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization.
Xu L; Xu M; Ma Z; Wang K; Jung TP; Ming D
J Neural Eng; 2021 Aug; 18(4):. PubMed ID: 34407522
[No Abstract] [Full Text] [Related]
36. A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.
Le Goallec A; Tierney BT; Luber JM; Cofer EM; Kostic AD; Patel CJ
PLoS Comput Biol; 2020 May; 16(5):e1007895. PubMed ID: 32392251
[TBL] [Abstract][Full Text] [Related]
37. Predicting daily emergency department visits using machine learning could increase accuracy.
Gafni-Pappas G; Khan M
Am J Emerg Med; 2023 Mar; 65():5-11. PubMed ID: 36574748
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.
Du Z; Yang Y; Zheng J; Li Q; Lin D; Li Y; Fan J; Cheng W; Chen XH; Cai Y
JMIR Med Inform; 2020 Jul; 8(7):e17257. PubMed ID: 32628616
[TBL] [Abstract][Full Text] [Related]
40. Are Current Survival Prediction Tools Useful When Treating Subsequent Skeletal-related Events From Bone Metastases?
Pan YT; Lin YP; Yen HK; Yen HH; Huang CC; Hsieh HC; Janssen S; Hu MH; Lin WH; Groot OQ
Clin Orthop Relat Res; 2024 Mar; ():. PubMed ID: 38517402
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]