Manuscript Topics

Technological advances in genomics and imaging have led to an explosion of molecular and biomedical data from large numbers of samples. This rapid increase in data dimension is challenging traditional analysis methods. Machine Learning naturally appears as one of the main drivers of progress. During the last years, the field of Machine Learning grew rapidly, mainly due to improvements in its algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact on molecular biology and biomedical applications, where the great variety of tasks and data types enables us to get benefit from Machine Learning algorithms in many different ways, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms. In this issue, we will explore the potential of machine learning applied to molecular system biology and biomedicine. The list of possible topics includes, but is not limited to:

- Biomedical imaging data analysis;
- Medical image processing;
- Prediction and analysis of gene expression data;
- Protein functional analysis;
- Identification of biomarkers for diagnosis and prognosis

Keywords: Machine learning; molecular biology; medical imaging; functional analysis; bioinformatics

Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/