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Protein Transport

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An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.

Current protein & peptide science
The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae, which plays an important role in the photosynthesis process. Since the function of a protein correlates with its location, knowing its subchloroplast localizatio...

A reference library for assigning protein subcellular localizations by image-based machine learning.

The Journal of cell biology
Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morph...

Predicting protein subcellular location with network embedding and enrichment features.

Biochimica et biophysica acta. Proteins and proteomics
The subcellular location of a protein is highly related to its function. Identifying the location of a given protein is an essential step for investigating its related problems. Traditional experimental methods can produce solid determination. Howeve...

autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences.

Journal of chemical information and modeling
Deep learning has proven to be a powerful method with applications in various fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can use different deep learn...

DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment.

International journal of molecular sciences
Mitochondrial proteins are physiologically active in different compartments, and their abnormal location will trigger the pathogenesis of human mitochondrial pathologies. Correctly identifying submitochondrial locations can provide information for di...

Protein transfer learning improves identification of heat shock protein families.

PloS one
Heat shock proteins (HSPs) play a pivotal role as molecular chaperones against unfavorable conditions. Although HSPs are of great importance, their computational identification remains a significant challenge. Previous studies have two major limitati...

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins.

International journal of molecular sciences
Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments,...

Deep learning to design nuclear-targeting abiotic miniproteins.

Nature chemistry
There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of ...

Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep...

iDeepSubMito: identification of protein submitochondrial localization with deep learning.

Briefings in bioinformatics
Mitochondria are membrane-bound organelles containing over 1000 different proteins involved in mitochondrial function, gene expression and metabolic processes. Accurate localization of those proteins in the mitochondrial compartments is critical to t...