AIMC Topic: Saccharomyces cerevisiae
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Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus.
Traffic (Copenhagen, Denmark)
Aug 22, 2016
The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this descripti...
Effect of Protein Repetitiveness on Protein-Protein Interaction Prediction Results Using Support Vector Machines.
Journal of computational biology : a journal of computational molecular cell biology
Aug 16, 2016
BACKGROUND: There are many computational approaches to predict the protein-protein interactions using support vector machines (SVMs) with high performance. In fact, performance of currently reported methods are significantly over-estimated and affect...
TargetM6A: Identifying N-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.
IEEE transactions on nanobioscience
Aug 10, 2016
As one of the most ubiquitous post-transcriptional modifications of RNA, N-methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly imp...
Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.
Protein science : a publication of the Protein Society
Aug 9, 2016
Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high...
Machine Learning of Protein Interactions in Fungal Secretory Pathways.
PloS one
Jul 21, 2016
In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict prote...
NegGOA: negative GO annotations selection using ontology structure.
Bioinformatics (Oxford, England)
Jun 17, 2016
MOTIVATION: Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction met...
Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.
BioMed research international
May 23, 2016
We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein s...
Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.
Nucleic acids research
Feb 22, 2016
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the te...
Detection of overlapping protein complexes in gene expression, phenotype and pathways of Saccharomyces cerevisiae using Prorank based Fuzzy algorithm.
Gene
Jan 22, 2016
Proteins show their functional activity by interacting with other proteins and forms protein complexes since it is playing an important role in cellular organization and function. To understand the higher order protein organization, overlapping is an...
A Factor Graph Approach to Automated GO Annotation.
PloS one
Jan 15, 2016
As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly int...
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