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Protein Transport

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MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.

Computational intelligence and neuroscience
Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction...

Protein subcellular localization prediction using multiple kernel learning based support vector machine.

Molecular bioSystems
Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered...

Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.

G3 (Bethesda, Md.)
High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a ta...

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

Gene
Knowledge of subcellular locations of proteins is crucially important for in-depth understanding their functions in a cell. With the explosive growth of protein sequences generated in the postgenomic age, it is highly demanded to develop computationa...

HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source.

Proteomics
Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine lea...

Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC.

Journal of theoretical biology
Predicting protein subcellular location with support vector machine has been a popular research area recently because of the dramatic explosion of bioinformation. Though substantial achievements have been obtained, few researchers considered the prob...

DeepLoc: prediction of protein subcellular localization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of...

An introduction to deep learning on biological sequence data: examples and solutions.

Bioinformatics (Oxford, England)
MOTIVATION: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data...

DeepSig: deep learning improves signal peptide detection in proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.

pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

Bioinformatics (Oxford, England)
MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence informat...