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.

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.

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.

scholarly journals A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods

2020 ◽  
Vol 21 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Zheng-Xing Guan ◽  
Shi-Hao Li ◽  
Zi-Mei Zhang ◽  
Dan Zhang ◽  
Hui Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Genomics ◽  
Experimental Methods ◽  
Extraction Methods ◽  
Clear Understanding ◽  
Learning Methods ◽  
Accurate Identification ◽  
Rna Molecules ◽  
Machine Learning Methods ◽  
Benchmark Datasets

MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as timeconsuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.

scholarly journals Screening of Antibacterial Compounds With Novel Structure From The FDA Approved Drugs Using Machine Learning Methods

2021 ◽  
Author(s):  
Wen-Xing Li ◽  
Xin Tong ◽  
Peng-Peng Yang ◽  
Yang Zheng ◽  
Ji-Hao Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Antibacterial Drugs ◽  
Antibacterial Compounds ◽  
Machine Learning Methods ◽  
Antibacterial Compound ◽  
Approved Drugs ◽  
Novel Structures ◽  
Fda Approved Drugs

Abstract Background: Due to the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. The aim of this study is to construct an antibacterial compound predictor using machine learning methods to screen potential antibacterial drugs.Methods: Active and inactive antibacterial compounds were acquired from the ChEMBL and PubChem database, which were used to construct benchmark datasets. The antibacterial compound predictor is constructed using the support vector machine (SVM), random forest (RF), and multi-layer perception (MLP) methods. We predicted the antibacterial activity of the Food and Drug Administration (FDA) approved drugs in the DrugBank database and screened novel antibacterial drugs through structural similarity analysis.Results: In the initial screen process, the results suggested that the benchmark dataset based on FP2 molecular fingerprints, along with the SVM, RF, and MLP methods showed excellent prediction performance (mean AUC > 0.9 for all models). Using the combination of these three models, a total of 957 potential antibacterial drugs were predicted. Most of the predicted drugs showed low structural similarities compared with the FDA approved antibacterial drugs. We finally screened 9 predicted antibacterial drugs with novel structures including 2 anti-tumor drugs (cyclophosphamide and ifosfamide), 2 ophthalmic drugs (apraclonidine and echothiophate) and 5 anesthetics (desflurane, enflurane, isoflurane, methoxyflurane, and sevoflurane).Conclusions: This study provides a new insight for predicting antibacterial compounds with novel structures by using FDA approved drugs. The predicted compounds with novel structures are worthy of further experimental verification in the future.

scholarly journals EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING

2020 ◽  
Vol V-3-2020 ◽  
pp. 817-824
Author(s):  
R. Roscher ◽  
B. Bohn ◽  
M. F. Duarte ◽  
J. Garcke
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Simulated Data ◽  
High Accuracy ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Recent Advances ◽  
Post Hoc

Abstract. For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.

Identification of long noncoding RNAs with machine learning methods: a review

2021 ◽  
Author(s):  
Lei Xu ◽  
Shihu Jiao ◽  
Dandan Zhang ◽  
Song Wu ◽  
Haihong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Noncoding Rnas ◽  
Experimental Methods ◽  
Long Noncoding Rnas ◽  
Biological Functions ◽  
Prediction Tools ◽  
Machine Learning Methods ◽  
High Resource ◽  
Future Work

Abstract Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.

scholarly journals Intelligent health risk prediction systems using machine learning: a review

2018 ◽  
Vol 7 (3) ◽  
pp. 1019 ◽  
Author(s):  
Mr Santosh A. Shinde ◽  
Dr P. Raja Rajeswari
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Research Review ◽  
Learning Methods ◽  
Research Directions ◽  
Machine Learning Methods ◽  
Experience Machine ◽  
Learning Techniques ◽  
Prediction Systems ◽  
Mother Earth

Humans are considered to be the most intelligent species on the mother earth and are inherently more health conscious. Since Centuries mankind has discovered various proven healthcare systems. To automate the process and predict diseases more accurately machine learning methods are gaining popularity in research community. Machine Learning methods facilitate development of the intelligence into a machine, so that it can perform better in the future using the learned experience. Machine learning methods application on electronic health record dataset could provide valuable information and predication of health risks.The aim of this research review paper are four-fold: i) serve as a guideline for researchers who are new to machine learning area and want to contribute to it, ii) provide state-of-the-art survey of machine learning, iii) application of machine learning techniques in the health prediction, and iv) provides further research directions required into health prediction system using machine learning. 

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.

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.

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.

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