AIMC Topic: Saccharomyces cerevisiae

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Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.

PLoS computational biology Jan 7, 2021
The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for ...
Algorithms Animals Single-Cell Analysis Humans Databases, Genetic Cell Line Supervised Machine Learning Saccharomyces cerevisiae RNA-Seq Mice Genomics
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Graph embeddings on gene ontology annotations for protein-protein interaction prediction.

BMC bioinformatics Dec 16, 2020
BACKGROUND: Protein-protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. ...
Humans Computational Biology Area Under Curve Task Performance and Analysis Saccharomyces cerevisiae Animals ROC Curve Mice Molecular Sequence Annotation Gene Ontology Protein Interaction Mapping
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Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

Nature communications Dec 1, 2020
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression leve...
Drosophila melanogaster Regulatory Sequences, Nucleic Acid Mice Humans Bacteria Animals Evolution, Molecular RNA, Messenger Base Sequence Deep Learning Gene Expression Regulation Saccharomyces cerevisiae
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Identifying longevity associated genes by integrating gene expression and curated annotations.

PLoS computational biology Nov 30, 2020
Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are...
Saccharomyces cerevisiae Genes, Fungal Gene Ontology Caenorhabditis elegans Longevity Reproducibility of Results Animals Gene Expression Machine Learning Algorithms
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A convolutional neural network segments yeast microscopy images with high accuracy.

Nature communications Nov 12, 2020
The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exh...
Saccharomyces cerevisiae Cell Cycle Software Image Processing, Computer-Assisted Microscopy Neural Networks, Computer
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Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice.

Bioprocess and biosystems engineering Sep 29, 2020
A hybrid neural model (HNM) and particle swarm optimization (PSO) was used to optimize ethanol production by a flocculating yeast, grown on cashew apple juice. HNM was obtained by combining artificial neural network (ANN), which predicted reaction sp...
Malus Models, Biological Fruit and Vegetable Juices Neural Networks, Computer Saccharomyces cerevisiae Ethanol
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A machine learning Automated Recommendation Tool for synthetic biology.

Nature communications Sep 25, 2020
Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long develop...
Bayes Theorem Fatty Acids Synthetic Biology Biofuels Saccharomyces cerevisiae Metabolic Engineering Machine Learning Escherichia coli Beer Dodecanol
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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nature communications Sep 25, 2020
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...
Metabolic Engineering Genotype Metabolic Networks and Pathways Amino Acids Saccharomyces cerevisiae Biochemical Phenomena Models, Biological Algorithms Tryptophan Phenotype Machine Learning Biosensing Techniques
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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Nature communications Aug 21, 2020
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identif...
Humans Machine Learning Drug Delivery Systems Drug Discovery Protein Binding Proteomics Saccharomyces cerevisiae Phosphotransferases Cell Survival Proteome Proteolysis Computational Biology Botrytis Ligands Mass Spectrometry Binding Sites HeLa Cells
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A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.

Proceedings of the National Academy of Sciences of the United States of America Jul 16, 2020
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear th...
Models, Biological Machine Learning Saccharomyces cerevisiae Transcriptome Metabolic Flux Analysis Systems Biology
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