Application of Information Theory in Biomedical Data Mining II
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".
Deadline for manuscript submissions: closed (12 July 2022) | Viewed by 350
Special Issue Editor
Interests: evolutionary computing; bioinformatics; computational biology; artificial evolution; machine learning; complex networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the era of big data in biomedicine, we now have access to high-throughput, high-dimensional, complex data to help us better understand the biology of living systems. The availability of more data does not, however, guarantee more knowledge, unless more advanced and powerful data analysis tools are developed to help us to mine the data and extract that knowledge. The high-dimensionality, heterogeneity, and complexity of biomedical big data renders many traditional statistical and computational methods obsolete and thus the area of biomedical data mining calls for new algorithms and methods that embrace complexity.
Information theory originates from information science and was developed to quantify, store, and transmit information. Information theoretical measures have been used to quantify correlations and interactions of attributes in biomedical data mining and hold great potential.
In this Special Issue, we would like to feature a series of novel applications of information theoretical measures for biomedical data mining. We welcome any original articles related to, but not limited to, the topics described herein.
Dr. Ting Hu
Guest Editor
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Keywords
- information theory
- entropy
- mutual information
- information gain
- biomedicine
- big data
- data mining
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