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Databases, Protein

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l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification.

IEEE/ACM transactions on computational biology and bioinformatics
SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental reposi...

Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

Journal of bioinformatics and computational biology
Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually pre...

Predicting protein function and other biomedical characteristics with heterogeneous ensembles.

Methods (San Diego, Calif.)
Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the va...

Enhancing protein function prediction with taxonomic constraints--The Argot2.5 web server.

Methods (San Diego, Calif.)
Argot2.5 (Annotation Retrieval of Gene Ontology Terms) is a web server designed to predict protein function. It is an updated version of the previous Argot2 enriched with new features in order to enhance its usability and its overall performance. The...

Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets.

IEEE/ACM transactions on computational biology and bioinformatics
Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting si...

Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models.

Journal of theoretical biology
Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders...

Accurate contact predictions using covariation techniques and machine learning.

Proteins
Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effectiv...

GoFDR: A sequence alignment based method for predicting protein functions.

Methods (San Diego, Calif.)
In this study, we developed a method named GoFDR for predicting Gene Ontology (GO)-based protein functions. The input for GoFDR is simply a query sequence-based multiple sequence alignment (MSA) produced by PSI-BLAST. For each GO term annotated to th...

MetazSecKB: the human and animal secretome and subcellular proteome knowledgebase.

Database : the journal of biological databases and curation
The subcellular location of a protein is a key factor in determining the molecular function of the protein in an organism. MetazSecKB is a secretome and subcellular proteome knowledgebase specifically designed for metazoan, i.e. human and animals. Th...

Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.

PLoS computational biology
Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phos...