Machine Learning Supervised Algorithms in Bioinformatics
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 9669
Special Issue Editor
Special Issue Information
Dear Colleagues,
With the advent of massive sequencing technologies and, more generally, increasingly sophisticated high-throughput experimental platforms, the amount of raw biological data produced in recent years has grown dramatically. The data deluge has led to the implementation and testing of powerful computational methods such as machine learning techniques to detect the complexity of biological data. Moreover, the contribution of omics technologies coupled with other heterogeneous data, such as phenotypic, structural and imaging data, helps to shed light on the enormous molecular complexity of living beings, although many aspects are still not characterized, since there are much missing data. Supervised machine learning algorithms that learn correlations between variables in annotated training data and use this information to predict inferred annotations for new data have already been used with great success in bioinformatics to predict biological events with greater reliability and accuracy than traditional algorithms.
This Special Issue is open to contributions concerning challenging research in different bioinformatics areas, (e.g., disease and health genomics, genomics and transcriptomics of both models and non-model organisms, eco-evolutionary genomics and phylogenomics, epigenomics, metagenomics, structural biology and bioimaging), addressed with supervised machine learning tools and algorithms.
This Special Issue on "Supervised Machine Learning Algorithms in Bioinformatics" aims to represent a wide range of topics related to theoretical, experimental, methodological or data contributions, or systematic reviews if they provide substantial contributions to the state-of-the-art. Topics include, but are not limited to, applications in bioinformatics of ML (machine learning) algorithms, such as SVM, KNN, regression or random forest and DL (deep learning) algorithms, such as, for example, RNN or CNN.
Dr. Tiziana Castrignanò
Guest Editor
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Keywords
- bioinformatics
- machine learning
- computational biology
- genomics
- transcriptomics
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