AIMC Topic: Saccharomyces cerevisiae
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Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.
BMC bioinformatics
Jan 11, 2016
BACKGROUND: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly con...
Semi-Supervised Multi-View Learning for Gene Network Reconstruction.
PloS one
Dec 7, 2015
The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently ...
Machine Learning approach to discriminate Saccharomyces cerevisiae yeast cells using sophisticated image features.
Journal of integrative bioinformatics
Oct 6, 2015
In biological research, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, Image analysis platforms are developed to address these problems and to of...
Genome-Wide Detection and Analysis of Multifunctional Genes.
PLoS computational biology
Oct 5, 2015
Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular...
Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.
BMC bioinformatics
Aug 25, 2015
BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once...
Protein complex detection in PPI networks based on data integration and supervised learning method.
BMC bioinformatics
Aug 25, 2015
BACKGROUND: Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict...
Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 20, 2015
Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting mod...
Computationally predicting protein-RNA interactions using only positive and unlabeled examples.
Journal of bioinformatics and computational biology
Feb 8, 2015
Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development...
Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.
Molecular biology of the cell
Jun 1, 2025
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
May 31, 2025
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. ...
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