scholarly journals Current Developments in Machine Learning Techniques in Biological Data Mining

2017 ◽  
Vol 11 ◽  
pp. 117793221668754 ◽  
Author(s):  
Gerard G Dumancas ◽  
Indra Adrianto ◽  
Ghalib Bello ◽  
Mikhail Dozmorov
Keyword(s):  
Machine Learning ◽  
Biomarker Discovery ◽  
Wide Spectrum ◽  
Specific Area ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Indispensable Tool ◽  
Underlying Mechanisms ◽  
Applications Of Machine Learning

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

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.

Role of Artificial Intelligence in Modeling Psychometrics to Detect Neurodevelopmental Disorders

2020 ◽  
pp. 213-227
Author(s):  
Navjot Singh ◽  
Amarjot Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodevelopmental Disorders ◽  
Machine Learning Techniques ◽  
Diagnosis And Prognosis ◽  
Processing Power ◽  
Learning Techniques ◽  
Numerous Data ◽  
Applications Of Machine Learning ◽  
Micro Patterns

The objective of the present chapter is to highlight applications of machine learning and artificial intelligence (AI) in clinical diagnosis of neurodevelopmental disorders. The proposed approach aims at recognizing behavioral traits and other cognitive aspects. The availability of numerous data and high processing power, such as graphic processing units (GPUs) or cloud computing, enabled the study of micro-patterns hundreds of times faster compared to manual analysis. AI, being a new technological breakthrough, enables study of human behavior patterns, which are hidden in millions of micro-patterns originating from human actions, reactions, and gestures. The chapter will also focus on the challenges in existing machine learning techniques and the best possible solution addressing those problems. In the future, more AI-based expert systems can enhance the accuracy of the diagnosis and prognosis process.

Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques

2021 ◽  
pp. 249-263
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Morteza Nazari-Heris ◽  
Behnam Mohammadi-Ivatloo ◽  
Somayeh Asadi
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Machine Learning Techniques ◽  
Wind Speed Forecasting ◽  
Learning Techniques ◽  
Applications Of Machine Learning

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

An Overview of Machine Learning in Medical Image Analysis

2017 ◽  
pp. 36-58 ◽  
Author(s):  
Anand Narasimhamurthy
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Imaging ◽  
Health Informatics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Applications Of Machine Learning

Medical image analysis is an area which has witnessed an increased use of machine learning in recent times. In this chapter, the authors attempt to provide an overview of applications of machine learning techniques to medical imaging problems, focusing on some of the recent work. The target audience comprises of practitioners, engineers, students and researchers working on medical image analysis, no prior knowledge of machine learning is assumed. Although the stress is mostly on medical imaging problems, applications of machine learning to other proximal areas will also be elucidated briefly. Health informatics is a relatively new area which deals with mining large amounts of data to gain useful insights. Some of the common challenges in health informatics will be briefly touched upon and some of the efforts in related directions will be outlined.

Applications of Machine Learning Techniques in Disease Classification From Medical Images

2018 ◽  
pp. 318-337 ◽  
Author(s):  
Bikesh Kumar Singh ◽  
Satya Eswari Jujjavarapu
Keyword(s):  
Machine Learning ◽  
Medical Images ◽  
Radial Basis Function Network ◽  
Objective Evaluation ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Future Challenges ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning

Machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), radial basis function network (RBFN), random forest (RF), naive Bayes classifier, etc. have gained much attention in recent years due to their widespread applications in diverse fields. This chapter is focused on providing a comprehensive insight of various techniques employed for key areas of medical image processing and analysis. Different applications covered in this chapter include feature extraction, feature selection, and cancer classification in medical images. The authors present current practices and evaluation measures used for objective evaluation of different machine learning methods in context to above-mentioned applications. Various factors associated with acceptance/rejection of such automated systems by medical research community are discussed. The authors also discuss how the interaction between automated analysis systems and medical professionals can be improved for its acceptance in clinical practice. They conclude the chapter by presenting research gaps and future challenges.

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