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Saccharomyces cerevisiae Proteins

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A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in different organisms. Many supervised classifiers like Random Forest (RF) and Support Vector Machine (SVM) have been used for intra or inter-species interaction...

Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

Protein science : a publication of the Protein Society
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...

Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

Combinatorial chemistry & high throughput screening
BACKGROUND: The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big pro...

PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs dat...

Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph.

IEEE/ACM transactions on computational biology and bioinformatics
The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction aff...

Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates.

Molecular & cellular proteomics : MCP
Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is...

NewGOA: Predicting New GO Annotations of Proteins by Bi-Random Walks on a Hybrid Graph.

IEEE/ACM transactions on computational biology and bioinformatics
A remaining key challenge of modern biology is annotating the functional roles of proteins. Various computational models have been proposed for this challenge. Most of them assume the annotations of annotated proteins are complete. But in fact, many ...

Revealing protein functions based on relationships of interacting proteins and GO terms.

Journal of biomedical semantics
BACKGROUND: In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they interact with each other. However,...

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

International journal of molecular sciences
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular...