AIMC Topic: Yeasts

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A Complete Transfer Learning-Based Pipeline for Discriminating Between Select Pathogenic Yeasts from Microscopy Photographs.

Pathogens (Basel, Switzerland) May 21, 2025
Pathogenic yeasts are an increasing concern in healthcare, with species like often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identific...
Humans Neural Networks, Computer Machine Learning Microscopy Yeasts Photography Image Processing, Computer-Assisted Candida
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Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies.

Food research international (Ottawa, Ont.) Dec 30, 2024
Diverse species of yeasts are commonly associated with food and food production environments. The contamination of food products by spoilage yeasts poses significant challenges, leading to quality degradation and food loss. Similarly, the introductio...
Fermentation Yeasts Neural Networks, Computer Food Microbiology Microscopy Deep Learning
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Automated quantification of lipid contents of Lipomyces starkeyi using deep-learning-based image segmentation.

Bioresource technology Nov 16, 2023
Intracellular lipid droplets (LDs), subcellular organelles playing a role in long-term carbon storage, have immense potential in biofuel and dietary lipid production. Monitoring the state of LDs in living cells is of utmost importance for quick bioma...
Lipomyces Lipids Yeasts Deep Learning
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The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts.

Applied microbiology and biotechnology Dec 28, 2022
BACKGROUND: Monitoring jar fermenter-cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of the...
Bioreactors Oils Fermentation Deep Learning Yeasts
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A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Understanding the ageing process is a very challenging problem for biologists. To help in this task, there has been a growing use of classification methods (from machine learning) to learn models that predict whether a gene influences the process of ...
Yeasts Genome, Human Drosophila melanogaster Mice Computational Biology Uncertainty Machine Learning Algorithms Humans Aging Protein Interaction Maps Animals
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Convolutional neural network analysis of recurrence plots for high resolution melting classification.

Computer methods and programs in biomedicine May 5, 2021
BACKGROUND AND OBJECTIVE: High resolution melting (HRM) analysis is a rapid and correct method for identification of species, such as, microorganism, bacteria, yeast, virus, etc. HRM data are produced using real-time polymerase chain reaction (PCR) a...
Yeasts Neural Networks, Computer Support Vector Machine
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Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.

PloS one Mar 17, 2021
Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. He...
Yeasts Lab-On-A-Chip Devices Image Processing, Computer-Assisted Deep Learning Cell Division Molecular Imaging
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Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks.

Plant molecular biology Feb 1, 2021
We developed two CNNs for predicting ubiquitination sites in Arabidopsis thaliana, demonstrated their competitive performance, analyzed amino acid physicochemical properties and the CNN structures, and predicted ubiquitination sites in Arabidopsis. A...
Humans Neural Networks, Computer Protein Processing, Post-Translational Ubiquitination Amino Acids Mice Yeasts Arabidopsis Computational Biology Proteome Software Animals
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A fully automated deep learning pipeline for high-throughput colony segmentation and classification.

Biology open Jun 23, 2020
Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large...
Neural Networks, Computer Reproducibility of Results Image Processing, Computer-Assisted Sensitivity and Specificity Colony Count, Microbial Yeasts Deep Learning High-Throughput Screening Assays
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UPLC-ESI-MS/MS based identification and antioxidant, antibacterial, cytotoxic activities of aqueous extracts from storey onion (Allium cepa L. var. proliferum Regel).

Food research international (Ottawa, Ont.) Dec 30, 2019
Storey onion (Allium cepa L. var. proliferum Regel) is a variety of onion commonly grown in northern China that has not been researched in detail. This study aimed to identify the chemical compositions of storey onion aqueous extracts by UPLC-ESI-MS/...
Yeasts Free Radicals Hep G2 Cells Bacteria Onions Humans Tandem Mass Spectrometry Chromatography, Liquid Antineoplastic Agents, Phytogenic Anti-Bacterial Agents Antioxidants Aspergillus niger
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