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protein subcellular localization Prediction Model Based on Graph Convolutional Network

INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2022年第4期14卷 937-946页

作者： Zhang, Tianhao Gu, Jiawei Wang, Zeyu Wu, Chunguo Liang, Yanchun Shi, Xiaohu Univ Jilin Coll Comp Sci & Technol Changchun 130012 Peoples R China Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Zhuhai Coll Sci & Technol Sch Comp Sci Zhuhai 519041 Peoples R China

protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://***/GoodGuy398/GraphLoc. [GRAPHICS] .

关键词： protein subcellular localization Deep learning Graph convolutional network Multi-head attention

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Semi-supervised Predictive Clustering Trees for Multi-label protein subcellular localization 34th

Semi-supervised Predictive Clustering Trees for Multi-label ...

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34th Brazilian Conference on Intelligent Systems

作者： Alcantara, Leonardo U. Triguero, Isaac Cerri, Ricardo Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9783031790317;9783031790324

protein subcellular localization is an important classification task because the location of proteins in a cell is directly linked to their functions. Since a protein can act at two or more locations simultaneously, multi-label classification algorithms are necessary. The currently used algorithms are usually based on supervised learning, which presents some disadvantages such as (i) a need for a large amount of labeled instances for training;(ii) a waste of valuable information that labeled instances can provide;and (iii) a high cost involved in obtaining labeled instances for training. To overcome these disadvantages, semi-supervised learning can be applied, where classifiers exploit both labeled and unlabeled data. Thus, in this paper, we propose a new semi-supervised algorithm for multi-label protein subcellular localization. Our proposal is based on decision tree classifiers induced using predictive clustering trees. We investigate many semi-supervised protein subcellular localization scenarios to test whether unlabeled instances can improve the multi-label classification process. Our results show that the proposal can achieve competitive or better results when compared to the pure supervised version of the predictive clustering trees.

关键词： predictive clustering trees protein subcellular localization multi-label classification semi-supervised learning

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Prediction of protein subcellular localization Based on Microscopic Images via Multi-Task Multi-Instance Learning

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Chinese Journal of Electronics 2022年第5期31卷 888-896页

作者： ZHANG Pingyue ZHANG Mengtian LIU Hui YANG Yang Department of Computer Science and Engineering Shanghai Jiao Tong University

protein localization information is essential for understanding protein functions and their roles in various biological processes. The image-based prediction methods of protein subcellular localization have emerged in recent years because of the advantages of microscopic images in revealing spatial expression and distribution of proteins in cells. However, the image-based prediction is a very challenging task, due to the multi-instance nature of the task and low quality of images. In this paper, we propose a multi-task learning strategy and mask generation to enhance the prediction performance. Furthermore, we also investigate effective multi-instance learning *** collect a large-scale dataset from the Human protein Atlas database, and the experimental results show that the proposed multi-task multi-instance learning model outperforms both single-instance learning and common multi-instance learning methods by large margins.

关键词： multitask multiinstance learning model deep learning (artificial intelligence) mask generation protein subcellular localization medical image processing biological processes protein functions microscopic images spatial expression image-based prediction methods Human protein Atlas database single-instance learning proteins cellular biophysics

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Enhancing protein subcellular localization Prediction Through Multi-Feature Fusion

Enhancing Protein Subcellular Localization Prediction Throug...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhao, Kai Liang, Weiyang Bi, Xuehua Yu, Guanglei Quan, Na Zhang, Linlin College of Information Science and Engineering Xinjiang University Urumqi China College of Medical Engineering and Technology Xinjiang Medical University Urumqi China College of Software Enginnering Xinjiang University Urumqi China

ISBN: (纸本)9798350337020

Accurately determining the subcellular location of proteins is essential for comprehending their functions, as it provides crucial insights into biochemical pathways and regulatory mechanisms. Although some methods have achieved promising effects, there are still some negative aspects, such as inappropriate feature engineering. In this paper, we propose a method for predicting the subcellular location of proteins that combines multiple features taken from several data sources. Firstly, we obtain three features, Di-peptide Composition, Moran correlation and Conjoint-Triad, from the amino acid sequence. We also employ node2vec to extract features from protein-protein interaction networks and combine them with gene ontology. To eliminate redundant information between features, we then fuse the multiple features from different data source with an auto-encoder. Finally, we employ a supervised learning model, Wide and Deep, to predict the subcellular location of proteins. The experimental results demonstrate that our approach achieves higher accuracy than state-of-the-art methods. This approach provides a promising solution for accurately predicting the subcellular location of proteins. © 2023 IEEE.

关键词： auto-encoder deep learning feature fusion multi-classification protein subcellular localization

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A DEEP NEURAL NETWORK APPROACH FOR THE PREDICTION OF protein subcellular localization

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NEURAL NETWORK WORLD 2021年第1期31卷 29-45页

作者： Samson, A. B. P. Chandra, S. R. A. Manikant, M. Indian Sch Mines Dept Comp Sci & Engn Indian Inst Technol Dhanbad 826004 Jharkand India

The subcellular localization of proteins is an essential characteristic of human cells, which plays a vital part in understanding distinct functions and cells' biological processes. The abnormal protein subcellular localization affects protein functionality and may cause many human diseases ranging from metabolic disorders to cancer. Therefore, the prediction of subcellular locations of the proteins is an important task. Artificial neural network has become a popular research topic in machine learning that can achieve remarkable results in learning high-level latent traits. This paper proposes a deep neural network (DNN) model to predict the human protein subcellular locations. The DNN automatically learns high-level representations of abstract features and proteins by examining nonlinear relationships between different subcellular locations. The experimental results have shown that the proposed method gave better results compared with the classical machine learning techniques such as support vector machine and random forest. This model also outperformed the similar model, which uses stacked auto-encoder (SAE) with a softmax classifier.

关键词： protein subcellular localization feature representation classification machine learning deep learning

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A multi-scale multi-model deep neural network via ensemble strategy on high-throughput microscopy image for protein subcellular localization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷

作者： Ding, Jiaqi Xu, Junhai Wei, Jianguo Tang, Jijun Guo, Fei Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300350 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

protein subcellular locations are closely related to the function of proteins. By detecting the abnormalities in subcellular locations, we can infer the occurrence of some diseases and mine new drug targets. A large number of single-cell high-throughput microscopy images provide us with relevant resources for studying protein distribution patterns. However, in the existing image-based protein subcellular localization methods, the traditional techniques are lack of efficiency and accuracy, and the potential of deep learning methods has not been fully tapped. So in this study, we propose a multi-scale multi-model deep neural network via ensemble strategy for protein subcellular localization on single-cell high-throughput images. First of all, we employ a deep convolutional neural network as multi-scale feature extractor and use global average pooling to map extracted features at different stages into feature vectors, then concatenate these multi-scale features to form a multi-model structure for image classification. In addition, we add Squeeze-and-Excitation Blocks to the network to emphasize more informative features. What is more, we use an ensemble method to fuse the classification results from the multi-model structure to obtain the final subcellular location of each single-cell image. Experiments show the validity and effectiveness of our method on yeast cell images, it can significantly improve the accuracy of high-throughput microscopy image-based protein subcellular localization, and we achieve the classification accuracy of 0.9098 on the high-throughput microscopy images of yeast cells. In the work of protein subcellular localization, our method provides a framework for processing and classifying microscope images, and further lays the foundation for the study of protein and gene functions.

关键词： protein subcellular localization Deep learning Multi-scale multi-model Ensemble learning Yeast cell image

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protein subcellular localization Prediction

Protein Subcellular Localization Prediction

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作者： Elettra Barberis Emilio Marengo Marcello Manfredi

The elucidation of the subcellular localization of proteins is very important in order to deeply understand their functions. In fact, proteins activities are strictly correlated to the cellular compartment and microenvironment in which they are present. In recent years, several effective and reliable proteomics techniques and computational methods have been developed and implemented in order to identify the proteins subcellular localization. This process is often time-consuming and expensive, but the recent technological and bioinformatics progress allowed the development of more accurate and simple workflows to determine the localization, interactions, and functions of proteins. In the following chapter, a brief introduction on the importance of knowing subcellular localization of proteins will be presented. Then, sample preparation protocols, proteomic methods, data analysis strategies, and software for the prediction of proteins localization will be presented and discussed. Finally, the more recent and advanced spatial proteomics techniques will be shown. less

关键词： protein subcellular localization Prediction Spatial proteomics Data analysis protein subcellular localization Sample preparation protocols Software for prediction

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Prediction of protein subcellular localization from Microscopic Images via Few-Shot Learning 17th

Prediction of Protein Subcellular Localization from Microsco...

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17th International Symposium on Bioinformatics Research and Applications (ISBRA)

作者： Arcamone, Francesco Tu, Yanlun Yang, Yang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Ctr Brain Like Comp & Machine Intelligence 800 Dong Chuan Rd Shanghai 200240 Peoples R China Politecn Milan Piazza Leonardo da Vinci 32 I-20133 Milan Italy Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China

ISBN: (纸本)9783030914141;9783030914158

Benefitting from the breakthrough development of microscopy imaging techniques, various bio-microscopic images have accumulated rapidly for the past decade. Using computer vision and machine learning methods, biological activities and molecular functions can be interpreted from these images, thus image analysis has become more and more important in current life science research. A prominent difficulty in biological image analysis is the lack of annotation, and the test set even contains data from unseen classes, i.e. the open-set issue. The image-based protein subcellular localization is a typical open-set problem. There are tens of subcellular compartments in cells, while the labeled data may only consist of proteins from several major organelles. Till now, the open-set problem has been rarely studied for biomedical image data. The main goal of this study is to train a few-shot learningmodel for the recognition of protein subcellular localization from immunofluorescence images. We conduct experiments on a data set collected from Human protein Atlas (HPA) and the results show that the introduced system can provide accurate results even with a small handful of images for an unknown class in a multi-instance learning scenario.

关键词： protein subcellular localization Few-shot learning ArcNet Immunofluorescence images

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SIFLoc: a self-supervised pre-training method for enhancing the recognition of protein subcellular localization in immunofluorescence microscopic images

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BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbab605-bbab605页

作者： Tu, Yanlun Lei, Houchao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong Univ Shanghai Peoples R China

With the rapid growth of high-resolution microscopy imaging data, revealing the subcellular map of human proteins has become a central task in the spatial proteome. The cell atlas of the Human protein Atlas (HPA) provides precious resources for recognizing subcellular localization patterns at the cell level, and the large-scale annotated data enable learning via advanced deep neural networks. However, the existing predictors still suffer from the imbalanced class distribution and the lack of labeled data for minor classes. Thus, it is necessary to develop new methods for coping with these issues. We leverage the self-supervised learning protocol to address these problems. Especially, we propose a pre-training scheme to enhance the conventional supervised learning framework called SIFLoc. The pre-training is featured by a hybrid data augmentation method and a modified contrastive loss function, aiming to learn good feature representations from microscopic images. The experiments are performed on a large-scale immunofluorescence microscopic image dataset collected from the HPA database. Using the same deep neural networks as the classifier, the model pre-trained via SIFLoc not only outperforms the model without pre-training by a large margin but also shows advantages over the state-of-the-art self-supervised learning methods. Especially, SIFLoc improves the prediction accuracy for minor organelles significantly.

关键词： protein subcellular localization microscopic images self-supervised learning

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Human protein subcellular localization identification via fuzzy model on Kernelized Neighborhood Representation

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APPLIED SOFT COMPUTING 2020年 96卷 106596-106596页

作者： Ding, Yijie Tang, Jijun Guo, Fei Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300350 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

In traditional wet experiments, fluorescent proteins are generally used to detect subcellular localization of protein. However, it is time consuming and expensive for detecting large-scale biological data. Many computational biological methods have been developed to identify various subcellular localizations of proteins. In the last ten years, machine learning methods have been widely used in many research issues in the field of bioinformatics. In this work, Fuzzy Support Vector Machine based on Kernelized Neighborhood Representation (FSVM-KNR) is proposed to predict the subcellular localization of protein. proteins are represented via six types of features (PsePSSM, PSSM-DWT, PSSM-AB, PsePP, PP-DWT and PP-AB). These features are constructed kernels and combined with Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL). Then, Kernelized Neighborhood Representation (KNR) algorithm is proposed to filter outliers via fuzzy membership scores. At last, the membership scores (with KNR) and integrated kernel (with KTA-MKL) are used to built FSVM-KNR model. To evaluate the performance of FSVM-KNR model, we test it on two benchmark datasets of protein subcellular localization. Our method achieves better performance (average precision: 0.7108 and 0.6916) on two datasets, respectively. In addition, our model is also compared with other FSVM model on 8 UCI datasets and the performance of FSVM-KNR is better or comparable. (C) 2020 Elsevier B.V. All rights reserved.

关键词： protein subcellular localization Fuzzy support vector machine Kernelized neighborhood representation protein feature extraction Multiple kernel fusion

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