scholarly journals Special Issue on Machine Learning for Biomedical Data Analysis

2019 ◽  
Vol 9 (21) ◽  
pp. 4676
Author(s):  
Federico Divina ◽  
Francisco Gómez-Vela
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Biomedical Data ◽  
Special Issue ◽  
Biomedical Data Analysis

In our world, increasing amounts of data are produced everyday [...]

Special issue on intelligent biomedical data analysis and processing

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Deepak Gupta ◽  
Joel J. P. C. Rodrigues ◽  
Oscar Castillo
Keyword(s):  
Data Analysis ◽  
Biomedical Data ◽  
Special Issue ◽  
Biomedical Data Analysis

Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 49-66
Author(s):  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Pandit Byomakesha Dash ◽  
Danilo Pelusi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Processing ◽  
Biomedical Informatics ◽  
Learning Model ◽  
Biomedical Data ◽  
Fuzzy C Means ◽  
Biomedical Data Analysis ◽  
Machine Learning Model ◽  
Efficient Machine

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

Special issue on Bio-inspired optimization techniques for Biomedical Data Analysis: Methods and applications

2020 ◽  
Vol 95 ◽  
pp. 106672 ◽  
Author(s):  
Victor Hugo C. de Albuquerque ◽  
Deepak Gupta ◽  
Ivanoe De Falco ◽  
Giovanna Sannino ◽  
Nizar Bouguila
Keyword(s):  
Data Analysis ◽  
Optimization Techniques ◽  
Biomedical Data ◽  
Special Issue ◽  
Biomedical Data Analysis ◽  
Analysis Methods ◽  
Data Analysis Methods

scholarly journals Special Issue on Intelligent Biomedical Data Analysis and Processing

2021 ◽  
Vol 15 (1) ◽  
pp. 13-17
Author(s):  
Deepak Gupta ◽  
Oscar Castillo ◽  
Ashish Khanna
Keyword(s):  
Data Analysis ◽  
Biomedical Data ◽  
Special Issue ◽  
Biomedical Data Analysis

scholarly journals Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine

2021 ◽  
Vol 17 (6) ◽  
pp. e1009014
Author(s):  
Qiang Gu ◽  
Anup Kumar ◽  
Simon Bray ◽  
Allison Creason ◽  
Alireza Khanteymoori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Large Scale ◽  
Supervised Machine Learning ◽  
Biomedical Data ◽  
Web Browser ◽  
Biomedical Data Analysis ◽  
Link Type ◽  
The World ◽  
The Galaxy

Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

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