Machine Learning and Bioinformatics in Human Health and Disease: 2nd Edition
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 4383
Special Issue Editor
Interests: cardiology; epidemiology; virology; adipose tissue; metabolism; nutrition; data science; diabetes; renal disease; biomarker
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning and bioinformatics have emerged as powerful tools for analyzing complex biological data and driving advances in human health and disease research. These fields offer a range of techniques for learning from and making predictions based on biological data, including genetic sequencing data, protein structure data, and medical imaging data.
In the context of human health and disease, machine learning and bioinformatics can be used to identify biomarkers for diseases, predict treatment outcomes, and develop new therapies. For example, machine learning algorithms can be used to analyze large datasets of patient information and identify patterns and correlations that might be missed by human experts. Bioinformatics techniques can be utilized to analyze genomic data and identify genetic variations that may contribute to disease.
Machine learning and bioinformatics techniques can also be used to analyze medical images, such as MRIs and CT scans, to identify structural changes associated with disease or injury. This can enable earlier and more accurate diagnoses, as well as more personalized treatment plans tailored to the specific needs of each patient.
However, as is the case with any computational method, machine learning and bioinformatics techniques have their limitations and challenges. One challenge is the need for large amounts of high-quality data to train and validate the algorithms. Another challenge is the potential for overfitting, where the algorithms learn patterns specific to the training data that cannot be applied to new data.
Despite these challenges, the potential benefits of applying machine learning and bioinformatics techniques to human health and disease research are extensive. This Special Issue will provide a platform for researchers to share their latest findings, insights, and innovations in this rapidly evolving field.
Potential topics include, but are not limited to, the following:
- Machine learning approaches for identifying genetic risk factors for common diseases;
- Analysis of single-cell RNA sequencing data using bioinformatics and machine learning techniques;
- Machine learning for predicting drug interactions and side effects;
- Bioinformatics and machine learning for precision medicine in cancer treatment;
- Machine learning-based diagnosis of neurodegenerative diseases using neuroimaging data;
- Integrating multi-omics data using machine learning techniques for disease diagnosis and treatment;
- Development of predictive models for infectious disease outbreaks using machine learning and epidemiological data;
- Application of machine learning and deep learning techniques in medical image analysis for disease diagnosis and treatment planning;
- Identifying disease biomarkers using bioinformatics, deep learning, and machine learning approaches;
- Machine learning approaches for predicting patient outcomes and disease progression.
This Special Issue is supervised by Dr. Andreas Leiherer and assisted by our Topical Advisory Panel Member Dr. Bernhard Bermeitinger (University of St. Gallen).
Dr. Andreas Leiherer
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- bioinformatics
- deep learning
- multi-omics approaches
- genetic risk factors
- drug interactions
- precision medicine
- neurodegenerative diseases
- disease diagnosis
- epidemic prediction
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