AIMC Topic: Saccharomyces cerevisiae

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Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.

Molecular biology of the cell
The life cycle of eukaryotic microorganisms involves complex transitions between states such as dormancy, mating, meiosis, and cell division, which are often studied independently from each other. Therefore, most microbial life cycles are theoretical...

Protein function prediction using GO similarity-based heterogeneous network propagation.

Scientific reports
Protein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. Despite significant advances, challenges persist due to data sparsity and functional ambiguity. ...

Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.

Journal of proteomics
Chromatin remodeling complexes, such as the Saccharomyces cerevisiae INO80 complex, exemplify how dynamic protein interaction networks govern cellular function through a balance of conserved structural modules and context-dependent functional partner...

A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology.

Scientific reports
Detecting protein complexes is crucial in computational biology for understanding cellular mechanisms and facilitating drug discovery. Evolutionary algorithms (EAs) have proven effective in uncovering protein complexes within networks of protein-prot...

Yeast Knowledge Graphs Database for Exploring Saccharomyces Cerevisiae and Schizosaccharomyces Pombe.

Journal of molecular biology
Biomedical literature contains an extensive wealth of information on gene and protein function across various biological processes and diseases. However, navigating this vast and often restricted-access data can be challenging, making it difficult to...

Optimization process of coffee pulp wines combined with the artificial neural network and response surface methodology.

Scientific reports
Coffee pulp wine was made from coffee pulp. The level range of fermentation factors was determined by one-factor experiment. The significance factors affecting fermentation were screened by Plackett-Burman and steepest climbing experiments, which wer...

High throughput variant libraries and machine learning yield design rules for retron gene editors.

Nucleic acids research
The bacterial retron reverse transcriptase system has served as an intracellular factory for single-stranded DNA in many biotechnological applications. In these technologies, a natural retron non-coding RNA (ncRNA) is modified to encode a template fo...

Deep learning meets histones at the replication fork.

Cell
Epigenetic inheritance of heterochromatin requires transfer of parental H3-H4 tetramers to both daughter duplexes during replication. Three recent papers exploit yeast genetics coupled to inheritance assays and AlphaFold2-multimer predictions coupled...

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Journal of mass spectrometry : JMS
Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to...

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network.

Briefings in bioinformatics
While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins m...