Bioinformatics and Machine Learning in Disease Research
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 25174
Special Issue Editor
2. Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2113, Australia
Interests: bioinformatics; transcriptomics; genomics; clinical genomics
Special Issue Information
Dear Colleagues,
Genomics is forecast to be the largest source of Big Data by 2025, totaling up to 40 exabytes per year and exceeding all current sources of Big Data combined (astronomy, YouTube, and Twitter). When other bioinformatics data are included in this, the vast volumes we have at our disposal become clear. ‘Data is the new gold’ is an often-used phrase that speaks to the value we can gain from such rich datasets, but to create this value, we need to effectively mine the data using innovative machine learning approaches.
Many prevalent human diseases are driven by multiple and complex factors, including diabetes, cardiovascular disease, and cancer. To understand the complex interplay of genetic and environmental factors driving these diseases, we need data-driven approaches which cater to large datasets, and machine learning offers us exactly this. Given the huge potential of machine learning and bioinformatics to understand human disease, a Special Issue of the journal Genes is being launched to explore the methods and applications of machine learning and bioinformatics in human disease. Authors are encouraged to submit original manuscripts describing the utilization of machine learning and bioinformatics to answer scientific questions relating to human disease. Also encouraged are papers describing new methods and reviews or comparisons of machine learning approaches in the context of human disease.
Dr. Natalie A. Twine
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- bioinformatics
- human disease
- genomics
- proteomics
- transcriptomics
- big data
- complex genetic disease
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