Bioinformatics and Biomedical Informatics are frontiers and interdisciplinary subjects derived from the theories and methodologies of comprehensive computer science, life science and biology, which play integral roles in disease diagnosis and therapy. In recent years, the fields of medical science and health informatics have made great progress and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data, which are no longer competent through traditional analytical methods. On the other hand, algorithms in bioinformatics and biomedical Informatics analysis have been significantly improved thanks to the rapid development of deep learning (including convolutional neural networks, recurrent neural networks, auto-encoders, generative adversarial networks, and so on). Accordingly, the application of deep learning in bioinformatics and biomedical Informatics to gain insight from data has been emphasised in both academic and medical fields.
At present, due to the rapid development of biotechnology in the historical period, the biomedical data generated in various research and application fields has increased exponentially, ranging from molecular level (gene functions, protein interactions, metabolic pathway, etc.), biological tissue level (brain connectivity map, X-ray images, magnetic resonance images, etc.), clinical level (intensive care unit, electronic medical record, etc.). The unneglectable fact is that growth speed and heterogeneous structure make it much more challenging to handle biomedical data with such properties than conventional data analysis methods as usual. Therefore, it is necessary to establish more powerful theoretical methods and practical tools for analysing and extracting meaningful information from above mentioned complex bio-data. Analysing these complex and heterogeneous data is a typical complex system problem. We need to analyse the dependence, relationship, or interaction between different levels of data and its environment. In this case, due to the non-linear, emergent, spontaneous order, adaptation, and feedback loop characteristics of the raw data, it is difficult for us to model by traditional methods. Only through deep learning can we solve these problems.
This Issue seeks to highlight the latest developments in applying advanced deep learning techniques in bioinformatics and biomedical Informatics analysis. Both original research papers and review articles related to deep learning in genomics and medical images analysis will be considered for publication