AIMC Topic: Saccharomyces cerevisiae
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Image-based fuzzy logic control for pressure-driven droplet microfluidics as autosampler for multimodal imaging microscopy.
Lab on a chip
Jan 14, 2025
Here we present a highly customisable image-based fuzzy logic control (FLC) method for pressure-driven droplet microfluidics. The system is designed to position droplets of different sizes in microfluidic chips of varying channel size in the centre o...
[PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies.
Bulletin of mathematical biology
Dec 6, 2024
The prion phenotype in yeast manifests as a white, pink, or red color pigment. Experimental manipulations destabilize prion phenotypes, and allow colonies to exhibit (red) sectored phenotypes within otherwise completely white colonies. Further inve...
Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.
Nature aging
Dec 3, 2024
Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and...
Graph-based machine learning model for weight prediction in protein-protein networks.
BMC bioinformatics
Nov 8, 2024
Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The...
PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of ...
Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.
Nature communications
Aug 21, 2024
We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usuall...
Delineating yeast cleavage and polyadenylation signals using deep learning.
Genome research
Aug 20, 2024
3'-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared with the well-defi...
Deep-learning-based design of synthetic orthologs of SH3 signaling domains.
Cell systems
Aug 5, 2024
Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts....
Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.
International journal of molecular sciences
Aug 4, 2024
With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we st...
High-throughput classification of S. cerevisiae tetrads using deep learning.
Yeast (Chichester, England)
Jun 8, 2024
Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore-autonomous fluorescent m...
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