323 related articles for article (PubMed ID: 32675233)
21. Automated analysis of high-content microscopy data with deep learning.
Kraus OZ; Grys BT; Ba J; Chong Y; Frey BJ; Boone C; Andrews BJ
Mol Syst Biol; 2017 Apr; 13(4):924. PubMed ID: 28420678
[TBL] [Abstract][Full Text] [Related]
22. Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast.
Blank LM; Kuepfer L; Sauer U
Genome Biol; 2005; 6(6):R49. PubMed ID: 15960801
[TBL] [Abstract][Full Text] [Related]
23. 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]
24. Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine.
Angione C
Biomed Res Int; 2019; 2019():8304260. PubMed ID: 31281846
[TBL] [Abstract][Full Text] [Related]
25. ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks.
Millard P; Schmitt U; Kiefer P; Vorholt JA; Heux S; Portais JC
PLoS Comput Biol; 2020 Apr; 16(4):e1007799. PubMed ID: 32287281
[TBL] [Abstract][Full Text] [Related]
26. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.
Heavner BD; Price ND
PLoS Comput Biol; 2015 Nov; 11(11):e1004530. PubMed ID: 26566239
[TBL] [Abstract][Full Text] [Related]
27. Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.
Song HS; Reifman J; Wallqvist A
PLoS One; 2014; 9(11):e112524. PubMed ID: 25397773
[TBL] [Abstract][Full Text] [Related]
28. Yeast 5 - an expanded reconstruction of the Saccharomyces cerevisiae metabolic network.
Heavner BD; Smallbone K; Barker B; Mendes P; Walker LP
BMC Syst Biol; 2012 Jun; 6():55. PubMed ID: 22663945
[TBL] [Abstract][Full Text] [Related]
29. Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae.
Hjersted JL; Henson MA
IET Syst Biol; 2009 May; 3(3):167-79. PubMed ID: 19449977
[TBL] [Abstract][Full Text] [Related]
30. The pan-genome of Saccharomyces cerevisiae.
Li G; Ji B; Nielsen J
FEMS Yeast Res; 2019 Nov; 19(7):. PubMed ID: 31584649
[TBL] [Abstract][Full Text] [Related]
31. 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]
32. A review on machine learning principles for multi-view biological data integration.
Li Y; Wu FX; Ngom A
Brief Bioinform; 2018 Mar; 19(2):325-340. PubMed ID: 28011753
[TBL] [Abstract][Full Text] [Related]
33. Machine learning enables identification of an alternative yeast galactose utilization pathway.
Harrison MC; Ubbelohde EJ; LaBella AL; Opulente DA; Wolters JF; Zhou X; Shen XX; Groenewald M; Hittinger CT; Rokas A
Proc Natl Acad Sci U S A; 2024 Apr; 121(18):e2315314121. PubMed ID: 38669185
[TBL] [Abstract][Full Text] [Related]
34. An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.
Nandi S; Subramanian A; Sarkar RR
Mol Biosyst; 2017 Jul; 13(8):1584-1596. PubMed ID: 28671706
[TBL] [Abstract][Full Text] [Related]
35. Applications of computational modeling in metabolic engineering of yeast.
Kerkhoven EJ; Lahtvee PJ; Nielsen J
FEMS Yeast Res; 2015 Feb; 15(1):1-13. PubMed ID: 25156867
[TBL] [Abstract][Full Text] [Related]
36. A Method to Constrain Genome-Scale Models with 13C Labeling Data.
Martín HG; Kumar VS; Weaver D; Ghosh A; Chubukov V; Mukhopadhyay A; Arkin A; Keasling JD
PLoS Comput Biol; 2015 Sep; 11(9):e1004363. PubMed ID: 26379153
[TBL] [Abstract][Full Text] [Related]
37. Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering.
Presnell KV; Alper HS
Biotechnol J; 2019 Sep; 14(9):e1800416. PubMed ID: 30927499
[TBL] [Abstract][Full Text] [Related]
38. Bridging the gap between gene expression and metabolic phenotype via kinetic models.
Vital-Lopez FG; Wallqvist A; Reifman J
BMC Syst Biol; 2013 Jul; 7():63. PubMed ID: 23875723
[TBL] [Abstract][Full Text] [Related]
39. Improved prediction of gene expression through integrating cell signalling models with machine learning.
Al Taweraqi N; King RD
BMC Bioinformatics; 2022 Aug; 23(1):323. PubMed ID: 35933367
[TBL] [Abstract][Full Text] [Related]
40. Incorporating Machine Learning into Established Bioinformatics Frameworks.
Auslander N; Gussow AB; Koonin EV
Int J Mol Sci; 2021 Mar; 22(6):. PubMed ID: 33809353
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]