AIMC Topic: Saccharomyces cerevisiae
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DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.
Analytical chemistry
Mar 12, 2023
In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of metho...
Prediction of ethanol fermentation under stressed conditions using yeast morphological data.
Journal of bioscience and bioengineering
Jan 13, 2023
A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and cell morphology under conditions of high-sugar stress. In this study, we developed artificial intell...
MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction.
Computers in biology and medicine
Jan 3, 2023
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single da...
Automation of yeast spot assays using an affordable liquid handling robot.
SLAS technology
Dec 9, 2022
The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experimen...
DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis.
eLife
Aug 17, 2022
Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-c...
A deep learning framework for identifying essential proteins based on multiple biological information.
BMC bioinformatics
Aug 4, 2022
BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interacti...
Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.
Scientific reports
Jul 13, 2022
An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accu...
Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning.
Scientific reports
Jul 8, 2022
The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data....
SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.
BMC genomics
Jun 27, 2022
BACKGROUND: Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs info...
Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.
PLoS biology
May 27, 2022
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to em...
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