scholarly journals Multi-Site Protein Subcellular Localization Based on Deep Convolutional Neural Network

2019 ◽  
pp. 311-327
Author(s):  
Hanhan Cong ◽  
Hong Liu ◽  
Yuehui Chen ◽  
Yaou Zhao ◽  
Lei Wang
Keyword(s):  
Neural Network ◽  
Subcellular Localization ◽  
Ensemble Learning ◽  
Protein Delivery ◽  
Cellular Localization ◽  
Protein Localization ◽  
Protein Subcellular Localization ◽  
Protein Database ◽  
Classification Result ◽  
Features Fusion

Each part of internal structure of cells which is commonly mentioned as subcellular is highly ordered and interconnected has unique functions. The experiments show that deviated protein delivery to the corresponding subcellular causes of human disease. Studies of protein localization can clarify pathogenesis and find treatments. As protein subcellular localization has a very important position in the field of biology, the research in this area is extremely active. Most of the existing protein sub cellular localization methods are more suitable for single-site sub cellular localization. This paper proposed an algorithm based deep convolution neural network which is suit for multi-site protein subcellular localization and the algorithm is implemented on the human protein database to verify and analyze the performance. In order to further improve the classification result of the algorithm, it was combined ensemble learning and features fusion. It can be inferred from experiments that the proposed algorithm is effective in multi-site protein subcellular localization and the overall correct rate of classification is 59.13% which is higher than SAE, SVM and RF. The algorithm proposed in this paper is more uniform and less affected by the number of samples. When the data samples are different, the classification results will have a certain impact, but the overall classification is good. Besides ensemble learning and features fusion are effective for improving classification result.

scholarly journals Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 293
Author(s):  
Warin Wattanapornprom ◽  
Chinae Thammarongtham ◽  
Apiradee Hongsthong ◽  
Supatcha Lertampaiporn
Keyword(s):  
Subcellular Localization ◽  
Protein Localization ◽  
Imbalanced Data ◽  
Plant Protein ◽  
Feature Subset ◽  
Protein Subcellular Localization ◽  
Multiple Classifiers ◽  
Testing Dataset ◽  
Ensemble Machine Learning ◽  
Final Probability

The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

scholarly journals Organellar Maps Through Proteomic Profiling – A Conceptual Guide

2020 ◽  
Vol 19 (7) ◽  
pp. 1076-1087 ◽  
Author(s):  
Georg H. H. Borner
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Subcellular Localization ◽  
Protein Function ◽  
Protein Localization ◽  
Proteomic Profiling ◽  
Protein Subcellular Localization ◽  
Single Experiment ◽  
Flexible Approach ◽  
Resolution Cell

Protein subcellular localization is an essential and highly regulated determinant of protein function. Major advances in mass spectrometry and imaging have allowed the development of powerful spatial proteomics approaches for determining protein localization at the whole cell scale. Here, a brief overview of current methods is presented, followed by a detailed discussion of organellar mapping through proteomic profiling. This relatively simple yet flexible approach is rapidly gaining popularity, because of its ability to capture the localizations of thousands of proteins in a single experiment. It can be used to generate high-resolution cell maps, and as a tool for monitoring protein localization dynamics. This review highlights the strengths and limitations of the approach and provides guidance to designing and interpreting profiling experiments.

Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3760-3765 ◽  
Author(s):  
Ze Yue Wu ◽  
Yue Hui Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Subcellular Localization ◽  
Classification Problem ◽  
Research Field ◽  
Protein Subcellular Localization ◽  
Classification Problems ◽  
Prediction Ability ◽  
Increment Of Diversity ◽  
Artificial Neural

Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible and effective.

scholarly journals Bird Eye View of Protein Subcellular Localization Prediction

Life ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 347
Author(s):  
Ravindra Kumar ◽  
Sandeep Kumar Dhanda
Keyword(s):  
Subcellular Localization ◽  
Protein Localization ◽  
3D Structure ◽  
Computational Prediction ◽  
Learning Approaches ◽  
Protein Subcellular Localization ◽  
Comprehensive Listing ◽  
Protein Subcellular Localization Prediction ◽  
Structure And Functions ◽  
Localization Prediction

Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

scholarly journals A deep neural network approach for the prediction of protein subcellular localization

2021 ◽  
Vol 31 (1) ◽  
pp. 29-45
Author(s):  
Anosh Babu P. Samson ◽  
Sekhara Rao Annavarapu Chandra ◽  
Mani Manikant
Keyword(s):  
Neural Network ◽  
Subcellular Localization ◽  
Deep Neural Network ◽  
Network Approach ◽  
Protein Subcellular Localization ◽  
Neural Network Approach

scholarly journals Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito

2020 ◽  
Vol 21 (S8) ◽  
Author(s):  
Castrense Savojardo ◽  
Pier Luigi Martelli ◽  
Giacomo Tartari ◽  
Rita Casadio
Keyword(s):  
Subcellular Localization ◽  
Cellular Localization ◽  
Large Scale ◽  
Protein Localization ◽  
Functional Characterization ◽  
Experimental Information ◽  
Complete Characterization ◽  
Mitochondrial Proteins ◽  
Mitochondrial Localization

Abstract Background The prediction of protein subcellular localization is a key step of the big effort towards protein functional annotation. Many computational methods exist to identify high-level protein subcellular compartments such as nucleus, cytoplasm or organelles. However, many organelles, like mitochondria, have their own internal compartmentalization. Knowing the precise location of a protein inside mitochondria is crucial for its accurate functional characterization. We recently developed DeepMito, a new method based on a 1-Dimensional Convolutional Neural Network (1D-CNN) architecture outperforming other similar approaches available in literature. Results Here, we explore the adoption of DeepMito for the large-scale annotation of four sub-mitochondrial localizations on mitochondrial proteomes of five different species, including human, mouse, fly, yeast and Arabidopsis thaliana. A significant fraction of the proteins from these organisms lacked experimental information about sub-mitochondrial localization. We adopted DeepMito to fill the gap, providing complete characterization of protein localization at sub-mitochondrial level for each protein of the five proteomes. Moreover, we identified novel mitochondrial proteins fishing on the set of proteins lacking any subcellular localization annotation using available state-of-the-art subcellular localization predictors. We finally performed additional functional characterization of proteins predicted by DeepMito as localized into the four different sub-mitochondrial compartments using both available experimental and predicted GO terms. All data generated in this study were collected into a database called DeepMitoDB (available at http://busca.biocomp.unibo.it/deepmitodb), providing complete functional characterization of 4307 mitochondrial proteins from the five species. Conclusions DeepMitoDB offers a comprehensive view of mitochondrial proteins, including experimental and predicted fine-grain sub-cellular localization and annotated and predicted functional annotations. The database complements other similar resources providing characterization of new proteins. Furthermore, it is also unique in including localization information at the sub-mitochondrial level. For this reason, we believe that DeepMitoDB can be a valuable resource for mitochondrial research.

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