scholarly journals A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhiyu Tao ◽  
Yanjuan Li ◽  
Zhixia Teng ◽  
Yuming Zhao
Keyword(s):  
Computer Technology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Subject Area ◽  
Transport Proteins ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Classification Methods ◽  
Classification Result

With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.

scholarly journals Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Zhengping Liang ◽  
Rui Guo ◽  
Jiangtao Sun ◽  
Zhong Ming ◽  
Zexuan Zhu
Keyword(s):  
Ant Colony Optimization ◽  
Protein Function ◽  
Ant Colony Algorithm ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Ant Colony ◽  
Classification Model ◽  
Selection Strategy ◽  
Classification Rules ◽  
Ant Colony Optimization Algorithm

Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named hmAntMinerorder, for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.

scholarly journals Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

2017 ◽  
Author(s):  
Chunyan Yu ◽  
Xiaoxu Li ◽  
Hong Yang ◽  
Yinghong Li ◽  
Weiwei Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Discovery Rate ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Machine Learning Algorithms ◽  
Identification Accuracy ◽  
Homologous Proteins ◽  
Prediction Algorithms ◽  
False Discovery

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

scholarly journals SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks

2020 ◽  
Vol 36 (11) ◽  
pp. 3343-3349 ◽  
Author(s):  
Manaz Kaleel ◽  
Yandan Zheng ◽  
Jialiang Chen ◽  
Xuanming Feng ◽  
Jeremy C Simpson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Subcellular Localization ◽  
Convolutional Neural Networks ◽  
Protein Function ◽  
Secretory Pathway ◽  
Protein Function Prediction ◽  
Subcellular Location ◽  
Machine Learning Algorithms ◽  
Endomembrane System ◽  
Protein Subcellular Location

Abstract Motivation The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. Results Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75–0.86 outperforming the other state-of-the-art web servers we tested. Availability and implementation SCLpred-EMS is freely available for academic users at http://distilldeep.ucd.ie/SCLpred2/. Contact [email protected]

scholarly journals Efficient Fruits Classification Using Convolutional Neural Network

2021 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
ADNAN ADNAN ABIDIN ◽  
Hamzah Hamzah ◽  
Marselina Endah
Keyword(s):  
Good Accuracy ◽  
Research Topic ◽  
Classification Model ◽  
Classification Methods ◽  
Proposed Model ◽  
Traditional Classification ◽  
Promising Solution ◽  
Learning In Computer Vision ◽  
Effective Result

Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

scholarly journals Predicting Human Protein Function with Multi-task Deep Neural Networks

2018 ◽  
Author(s):  
Rui Fa ◽  
Domenico Cozzetto ◽  
Cen Wan ◽  
David T. Jones
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Protein Function ◽  
Deep Neural Networks ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Machine Learning Algorithms ◽  
Medium Size ◽  
Prediction Ability

AbstractMachine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning techniques to further enhance prediction ability.

scholarly journals VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

2022 ◽  
Vol 12 ◽  
Author(s):  
Yue Gong ◽  
Benzhi Dong ◽  
Zixiao Zhang ◽  
Yixiao Zhai ◽  
Bo Gao ◽  
...  
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Transition Probability ◽  
Vesicular Transport ◽  
Transport Proteins ◽  
Data Set ◽  
Other Information ◽  
Scoring Matrix ◽  
Test Sets ◽  
Matthew’S Correlation Coefficient

Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew’s correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

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