AIMC Topic: 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...
Genes, Fungal Gene Ontology Caenorhabditis elegans Animals Gene Expression Saccharomyces cerevisiae Longevity Machine Learning Algorithms Reproducibility of Results
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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...
Image Processing, Computer-Assisted Microscopy Neural Networks, Computer Saccharomyces cerevisiae Software Cell Cycle
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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...
Ethanol Models, Biological Fruit and Vegetable Juices Saccharomyces cerevisiae Malus Neural Networks, Computer
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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...
Synthetic Biology Biofuels Saccharomyces cerevisiae Escherichia coli Bayes Theorem Fatty Acids Metabolic Engineering Beer Dodecanol Machine Learning
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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...
Machine Learning Algorithms Phenotype Saccharomyces cerevisiae Biochemical Phenomena Metabolic Engineering Amino Acids Models, Biological Tryptophan Biosensing Techniques Genotype Metabolic Networks and Pathways
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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...
Phosphotransferases Botrytis Saccharomyces cerevisiae Proteolysis Binding Sites Computational Biology Proteomics Proteome Ligands HeLa Cells Mass Spectrometry Cell Survival Drug Delivery Systems Drug Discovery Protein Binding Humans Machine Learning
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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...
Machine Learning Transcriptome Systems Biology Models, Biological Saccharomyces cerevisiae Metabolic Flux Analysis
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An Artificial Neural Network Assisted Dynamic Light Scattering Procedure for Assessing Living Cells Size in Suspension.

Sensors (Basel, Switzerland) Jun 17, 2020
Dynamic light scattering (DLS) is an essential technique used for assessing the size of the particles in suspension, covering the range from nanometers to microns. Although it has been very well established for quite some time, improvement can still ...
Dynamic Light Scattering Scattering, Radiation Suspensions Cell Size Particle Size Light Saccharomyces cerevisiae Neural Networks, Computer
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Prediction of Protein-Protein Interactions with Local Weight-Sharing Mechanism in Deep Learning.

BioMed research international Jun 13, 2020
Protein-protein interactions (PPIs) are important for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. The experimental methods for identifying PPIs are always time-consuming and ex...
Escherichia coli Proteins Databases, Protein Saccharomyces cerevisiae Caenorhabditis elegans Protein Interaction Mapping Protein Interaction Maps Mice Computational Biology Humans Animals Deep Learning
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Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.

Analytical biochemistry May 28, 2020
Lysine 2-hydroxyisobutyrylation (K) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of K sites is a first...
Protein Processing, Post-Translational Oryza HeLa Cells Humans Machine Learning Seeds Lysine Hydroxybutyrates Bryopsida Saccharomyces cerevisiae Species Specificity
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