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.

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.

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 The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

scholarly journals Smart literature review: a practical topic modelling approach to exploratory literature review

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Claus Boye Asmussen ◽  
Charles Møller
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Modelling ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Literature Reviews ◽  
Modelling Approach

Abstract Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.

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.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Generalized model of software code`s static analysis based on machine learning for vulnerabilitys search

2020 ◽  
pp. 143-152 ◽  
Author(s):  
M.V. Buinevich ◽  
K.E. Izrailov
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Domain Model ◽  
Highly Qualified ◽  
Program Code ◽  
Learning Methods ◽  
Static And Dynamic Analysis ◽  
The Past ◽  
Machine Learning Methods ◽  
Software Code

Over the past years, the use of unsafe software, the search for vulnerabilities in which relies on static and dynamic analysis, continues to be the main threat to the infosphere. The manual form of conducting static analysis is extremely time-consuming and requires the involvement of highly qualified, and therefore deficient specialists. An alternative is the automation of the process based on artificial intelligence. This work is aimed at finding solutions for the use of machine learning methods at all stages of the static analysis of program code, for which the formal needs of the stages and the possibilities of the methods are studied and correlated. The main result of the study is a generalized domain model, and private — 14 solutions to the “key” problems of static analysis of program code using machine learning methods.

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.

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