307 related articles for article (PubMed ID: 35604554)
1. A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.
Vijayakumar S; Magazzù G; Moon P; Occhipinti A; Angione C
Methods Mol Biol; 2022; 2399():87-122. PubMed ID: 35604554
[TBL] [Abstract][Full Text] [Related]
2. A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria.
Vijayakumar S; Rahman PKSM; Angione C
iScience; 2020 Dec; 23(12):101818. PubMed ID: 33354660
[TBL] [Abstract][Full Text] [Related]
3. A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.
Culley C; Vijayakumar S; Zampieri G; Angione C
Proc Natl Acad Sci U S A; 2020 Aug; 117(31):18869-18879. PubMed ID: 32675233
[TBL] [Abstract][Full Text] [Related]
4. Machine and deep learning meet genome-scale metabolic modeling.
Zampieri G; Vijayakumar S; Yaneske E; Angione C
PLoS Comput Biol; 2019 Jul; 15(7):e1007084. PubMed ID: 31295267
[TBL] [Abstract][Full Text] [Related]
5. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives.
Vijayakumar S; Conway M; Lió P; Angione C
Methods Mol Biol; 2018; 1716():389-408. PubMed ID: 29222764
[TBL] [Abstract][Full Text] [Related]
6. Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data.
Magazzù G; Zampieri G; Angione C
Bioinformatics; 2021 Oct; 37(20):3546-3552. PubMed ID: 33974036
[TBL] [Abstract][Full Text] [Related]
7. Exploring synergies between plant metabolic modelling and machine learning.
Sampaio M; Rocha M; Dias O
Comput Struct Biotechnol J; 2022; 20():1885-1900. PubMed ID: 35521559
[TBL] [Abstract][Full Text] [Related]
8. Advances in flux balance analysis by integrating machine learning and mechanism-based models.
Sahu A; Blätke MA; Szymański JJ; Töpfer N
Comput Struct Biotechnol J; 2021; 19():4626-4640. PubMed ID: 34471504
[TBL] [Abstract][Full Text] [Related]
9. Genome-scale metabolic network models: from first-generation to next-generation.
Ye C; Wei X; Shi T; Sun X; Xu N; Gao C; Zou W
Appl Microbiol Biotechnol; 2022 Aug; 106(13-16):4907-4920. PubMed ID: 35829788
[TBL] [Abstract][Full Text] [Related]
10. Using machine learning approaches for multi-omics data analysis: A review.
Reel PS; Reel S; Pearson E; Trucco E; Jefferson E
Biotechnol Adv; 2021; 49():107739. PubMed ID: 33794304
[TBL] [Abstract][Full Text] [Related]
11. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
[TBL] [Abstract][Full Text] [Related]
12. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models.
Dahal S; Yurkovich JT; Xu H; Palsson BO; Yang L
Proteomics; 2020 Sep; 20(17-18):e1900282. PubMed ID: 32579720
[TBL] [Abstract][Full Text] [Related]
13. Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE).
Ma T; Zhang A
BMC Genomics; 2019 Dec; 20(Suppl 11):944. PubMed ID: 31856727
[TBL] [Abstract][Full Text] [Related]
14. Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.
Gonçalves DM; Henriques R; Costa RS
Comput Struct Biotechnol J; 2023; 21():4960-4973. PubMed ID: 37876626
[TBL] [Abstract][Full Text] [Related]
15. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
Lewis JE; Kemp ML
Nat Commun; 2021 May; 12(1):2700. PubMed ID: 33976213
[TBL] [Abstract][Full Text] [Related]
16. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.
Vijayakumar S; Conway M; Lió P; Angione C
Brief Bioinform; 2018 Nov; 19(6):1218-1235. PubMed ID: 28575143
[TBL] [Abstract][Full Text] [Related]
17. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods.
Magazzù G; Zampieri G; Angione C
Comput Biol Med; 2022 Dec; 151(Pt A):106244. PubMed ID: 36343407
[TBL] [Abstract][Full Text] [Related]
18. Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.
Cuperlovic-Culf M; Nguyen-Tran T; Bennett SAL
Methods Mol Biol; 2023; 2553():417-439. PubMed ID: 36227553
[TBL] [Abstract][Full Text] [Related]
19. Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium
Vijayakumar S; Angione C
STAR Protoc; 2021 Dec; 2(4):100837. PubMed ID: 34632416
[TBL] [Abstract][Full Text] [Related]
20. DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.
Chandrashekar PB; Alatkar S; Wang J; Hoffman GE; He C; Jin T; Khullar S; Bendl J; Fullard JF; Roussos P; Wang D
Genome Med; 2023 Oct; 15(1):88. PubMed ID: 37904203
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]