About this Research Topic
Gene function, including that of coding and non-coding genes, can be difficult to identify in molecular wet laboratories. Therefore, computational methods, often including machine learning, may be a useful tool to guide and predict function. Although machine learning has been considered as a “black box” in the past, it can be more accurate than simple statistical testing methods. In recent years, deep learning and big data machine learning techniques have developed rapidly and achieved an amazing level of performance in many areas including image classification and speech recognition. This Research topic will explore the potential for machine learning applied to gene function prediction.
We hope that code describing novel methodology and data from real world application can be presented together in this issue. The list of possible topics includes, but is not limited to:
- Latest machine learning algorithms on gene function prediction;
- Reviews or surveys with benchmark datasets in gene function prediction;
- Deep learning techniques with applications in gene function prediction;
- Non-coding gene functional computational analysis.
Keywords: Machine learning, Genetics, Bioinformatics, Feature selection, Deep learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.