scholarly journals A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization

Catalysts ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 291 ◽  
Author(s):  
Anamya Ajjolli Nagaraja ◽  
Philippe Charton ◽  
Xavier F. Cadet ◽  
Nicolas Fontaine ◽  
Mathieu Delsaut ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glass Ceiling ◽  
Principal Component ◽  
Enzyme Concentration ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Free System ◽  
Machine Learning Approach ◽  
Selection Of

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of “glass ceiling”. In order to explore this “glass ceiling” space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the “out-of-the-box” fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.

scholarly journals A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. A. Camilleri ◽  
S. B. Eickhoff ◽  
S. Weis ◽  
J. Chen ◽  
J. Amunts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Executive Functioning ◽  
Factor Model ◽  
Five Factor Model ◽  
Principal Component ◽  
Function System ◽  
Learning Approach ◽  
Psychometric Data ◽  
Machine Learning Approach

AbstractWhile a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis–Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.

scholarly journals Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252096
Author(s):  
Maria B. Rabaglino ◽  
Alan O’Doherty ◽  
Jan Bojsen-Møller Secher ◽  
Patrick Lonergan ◽  
Poul Hyttel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Focal Adhesion ◽  
Learning Approach ◽  
Omics Data ◽  
Machine Learning Approach ◽  
Cluster 2 ◽  
Omics Data Integration

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.

Predictive Model for Selection of Upper Treated Vertebra Using a Machine Learning Approach

2020 ◽  
Author(s):  
Renaud Lafage ◽  
Bryan Ang ◽  
Basel Sheikh Alshabab ◽  
Jonathan Elysee ◽  
Francis Lovecchio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Selection Of

scholarly journals Predicting the chemical reactivity of organic materials using a machine-learning approach

2020 ◽  
Vol 11 (30) ◽  
pp. 7813-7822 ◽  
Author(s):  
Byungju Lee ◽  
Jaekyun Yoo ◽  
Kisuk Kang
Keyword(s):  
Machine Learning ◽  
Organic Materials ◽  
Chemical Reactivity ◽  
Functional Materials ◽  
Chemical Components ◽  
Learning Approach ◽  
System P ◽  
Machine Learning Approach ◽  
Selection Of

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.

scholarly journals A machine learning approach to predict blastocyst formation in vitro

2019 ◽  
Vol 111 (4) ◽  
pp. e47
Author(s):  
N.C. Spies ◽  
E.E.A. Pisters ◽  
A.E. Ball ◽  
E.S. Jungheim ◽  
J.K. Riley
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Blastocyst Formation ◽  
Machine Learning Approach

scholarly journals PREDICTING SUCCESSFUL CONVENTIONAL IN VITRO FERTILIZATION WITH A MACHINE LEARNING APPROACH BASED ON MARKERS OF CAPACITATION

2020 ◽  
Vol 114 (3) ◽  
pp. e297
Author(s):  
Stephanie Gunderson ◽  
Nicholas C. Spies ◽  
Lis C. Puga Molina ◽  
Emily S. Jungheim ◽  
Joan Riley ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Learning Approach ◽  
Vitro Fertilization ◽  
Machine Learning Approach

scholarly journals Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4600
Author(s):  
Marco Beccaria ◽  
Flavio A. Franchina ◽  
Mavra Nasir ◽  
Theodore Mellors ◽  
Jane E. Hill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mycobacterium Tuberculosis Complex ◽  
Solid Phase ◽  
Ex Vivo ◽  
Quadrupole Mass ◽  
Tuberculosis Complex ◽  
Machine Learning Approach ◽  
Volatile Organic ◽  
Selection Of

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography–quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

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