A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

2020 ◽  
Vol 26 (26) ◽  
pp. 3049-3058
Author(s):  
Ting Liu ◽  
Hua Tang
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Future Development ◽  
Malaria Parasite ◽  
Mitochondrial Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Effective Drugs ◽  
Construction Strategies ◽  
Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review

2019 ◽  
Vol 20 (3) ◽  
pp. 217-223 ◽  
Author(s):  
Huan-Huan Wei ◽  
Wuritu Yang ◽  
Hua Tang ◽  
Hao Lin
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Cell Penetrating Peptides ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Drug Molecules ◽  
Development Direction ◽  
Cell Penetrating ◽  
Construction Strategies ◽  
Insight Into

Background:Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.Methods:Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.Results:We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.Conclusion:In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

scholarly journals Machine Learning Methods for Analysis Fraud Credit Card Transaction

2019 ◽  
Vol 8 (6S) ◽  
pp. 870-874
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Negative Impact ◽  
Online Banking ◽  
Data Sets ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
One Time Password ◽  
Day By Day ◽  
Isolation Forest

The use of online banking and credit card is increasing day by day. As the usage of credit/debit card or netbanking is increasing, the possibility of many fraud activities is also increasing. There are many incidents are happened in presently where because of lack of knowledge the credit card users are sharing their personal details, card details and one time password to a unknown fake call. And the result will be fraud happened with the account. Fraud is the problem that it is very difficult to trace the fraud person if he made call from a fake identity sim or call made by some internet services. So in this research some supervised methodologies and algorithms are used to detect fraud which gives approximate accurate results. The illegal or fraud activities put very negative impact on the business and customers loose trust on the company. It also affects the revenue and turnover of the company. In this research isolation forest algorithm is applied for classification to detect the fraud activities and the data sets are collected from the professional survey organizations.

Recent Advances on the Machine Learning Methods in Identifying Phage Virion Proteins

2020 ◽  
Vol 15 (7) ◽  
pp. 657-661
Author(s):  
Yingjuan Yang ◽  
Chunlong Fan ◽  
Qi Zhao
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Experimental Methods ◽  
Host Cells ◽  
Learning Methods ◽  
Research Directions ◽  
Antibacterial Drugs ◽  
Machine Learning Methods ◽  
Recent Advances

In the field of bioinformatics, the prediction of phage virion proteins helps us understand the interaction between phage and its host cells and promotes the development of new antibacterial drugs. However, traditional experimental methods to identify phage virion proteins are expensive and inefficient, more researchers are working to develop new computational methods. In this review, we summarized the machine learning methods for predicting phage virion proteins during recent years, and briefly described their advantages and limitations. Finally, some research directions related to phage virion proteins are listed.

Machine learning methods in prediction of protein palmitoylation sites: A brief review

2020 ◽  
Vol 26 ◽  
Author(s):  
Yanwen Li ◽  
Feng Pu ◽  
Jingru Wang ◽  
Zhiguo Zhou ◽  
Chunhua Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Mechanism ◽  
Computational Methods ◽  
Experimental Studies ◽  
Biological Processes ◽  
Biological Mechanism ◽  
Lipid Modification ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Protein Palmitoylation

: Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learning-based methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.

Recent Advances on Antioxidant Identification Based on Machine Learning Methods

2020 ◽  
Vol 21 (10) ◽  
pp. 804-809
Author(s):  
Pengmian Feng ◽  
Lijing Feng
Keyword(s):  
Machine Learning ◽  
Antioxidant Activity ◽  
Free Radicals ◽  
Computational Methods ◽  
Future Perspectives ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Recent Advances

Antioxidants are molecules that can prevent damages to cells caused by free radicals. Recent studies also demonstrated that antioxidants play roles in preventing diseases. However, the number of known molecules with antioxidant activity is very small. Therefore, it is necessary to identify antioxidants from various resources. In the past several years, a series of computational methods have been proposed to identify antioxidants. In this review, we briefly summarized recent advances in computationally identifying antioxidants. The challenges and future perspectives for identifying antioxidants were also discussed. We hope this review will provide insights into researches on antioxidant identification.

Applications of Machine Learning Methods in Drug Toxicity Prediction

2018 ◽  
Vol 18 (12) ◽  
pp. 987-997 ◽  
Author(s):  
Li Zhang ◽  
Hui Zhang ◽  
Haixin Ai ◽  
Huan Hu ◽  
Shimeng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Toxicity ◽  
Prediction Models ◽  
New Drugs ◽  
Toxicity Tests ◽  
Support Vector ◽  
Toxicity Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

Toxicity evaluation is an important part of the preclinical safety assessment of new drugs, which is directly related to human health and the fate of drugs. It is of importance to study how to evaluate drug toxicity accurately and economically. The traditional in vitro and in vivo toxicity tests are laborious, time-consuming, highly expensive, and even involve animal welfare issues. Computational methods developed for drug toxicity prediction can compensate for the shortcomings of traditional methods and have been considered useful in the early stages of drug development. Numerous drug toxicity prediction models have been developed using a variety of computational methods. With the advance of the theory of machine learning and molecular representation, more and more drug toxicity prediction models are developed using a variety of machine learning methods, such as support vector machine, random forest, naive Bayesian, back propagation neural network. And significant advances have been made in many toxicity endpoints, such as carcinogenicity, mutagenicity, and hepatotoxicity. In this review, we aimed to provide a comprehensive overview of the machine learning based drug toxicity prediction studies conducted in recent years. In addition, we compared the performance of the models proposed in these studies in terms of accuracy, sensitivity, and specificity, providing a view of the current state-of-the-art in this field and highlighting the issues in the current studies.

Computational Methods for Predicting DNA Binding Proteins

2020 ◽  
Vol 17 (4) ◽  
pp. 258-270
Author(s):  
Gaofeng Pan ◽  
Jiandong Wang ◽  
Liang Zhao ◽  
William Hoskins ◽  
Jijun Tang
Keyword(s):  
Machine Learning ◽  
Dna Binding ◽  
Computational Methods ◽  
Binding Proteins ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Dna Binding Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Classifier Algorithms

Background: DNA-binding proteins are very important to many biomolecular functions. The traditional experimental methods are expensive and time-consuming, so, computational methods that can predict whether a protein is a DNA-binding protein or not are very helpful to researchers. Machine learning has been widely used in many research areas. Many researchers have proposed machine learning methods for DNA-binding protein prediction, and this paper highlights their advantages and disadvantages. Objective: There are many computational methods that can predict DNA-binding proteins. Every method uses different features and different classifier algorithms. In this paper, a review of these methods is provided to find out some common procedures that can help researchers to develop more accurate methods. Methods: Firstly, the information stored in the protein sequence and gene sequence is presented. That information is the basis to find out the patterns leading to binding. Then, feature extraction methods and classifier algorithms are discussed. At last, some commonly used benchmark datasets are analysed and evaluated by methods. Conclusion: In this review, we analyzed some popular computational methods to predict DNAbinding protein. From those methods, we highlighted many features necessary to build up an accurate DNA-binding protein classifier. This can also help researchers to build up more useful computational tools. Currently, there are some machine learning methods with good performance in predicting DNAbinding proteins. The performance can be improved by using different kinds of features and classifiers.

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides

2021 ◽  
Author(s):  
Jing Xu ◽  
Fuyi Li ◽  
André Leier ◽  
Dongxu Xiang ◽  
Hsin-Hui Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Peptides ◽  
Computational Methods ◽  
Cross Validation ◽  
Predictive Performance ◽  
Support Vector ◽  
Data Sets ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods

Abstract Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.

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