AIMC Topic: Intracellular Space
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A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2021
Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are...
Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2020
Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging techn...
Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.
Nature communications
Dec 7, 2020
Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown t...
Image-based phenotyping of disaggregated cells using deep learning.
Communications biology
Nov 13, 2020
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undes...
Predicting protein subcellular location with network embedding and enrichment features.
Biochimica et biophysica acta. Proteins and proteomics
Jun 25, 2020
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...
PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins.
BioMed research international
Nov 19, 2019
Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategie...
Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features.
Scientific reports
Nov 6, 2018
Long non-coding RNAs are involved in biological processes throughout the cell including the nucleus, chromatin and cytosol. However, most lncRNAs remain unannotated and functional annotation of lncRNAs is difficult due to their low conservation and t...
iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features.
Journal of theoretical biology
Sep 21, 2017
Bacteriophage proteins are viruses that can significantly impact on the functioning of bacteria and can be used in phage based therapy. The functioning of Bacteriophage in the host bacteria depends on its location in those host cells. It is very impo...
pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.
Gene
Jul 18, 2017
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...
MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.
Computational intelligence and neuroscience
Jul 4, 2017
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...
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