Dear Colleagues,

The development of Artificial Intelligence (AI) pushes the boundaries of new computing paradigms to become actual realities for many science and engineering challenges. Biomedical big data provides excellent playgrounds and test fields for AI to exercise its maximum capacities. The advances of high-throughput biotechnologies have shifted the focus of biomedical science from studying individual molecules towards analyzing the interactions of the complex molecular and cellular networks that control whole biological systems. Big Data in biomedicine can be used to provide health profiles and predictive models around individual patients. The use of high-throughput data to integrate genetic and clinical inter-relationships; real-world data to infer biological principles as well as associations, trajectories and stratifications of patients; data-driven approaches for patients and digital platforms are the hope for medical problems and evidence-based medicine.

This special issue welcomes articles presenting novel developments in the field of artificial intelligence in biology and medicine, and their applications to the analysis of high-throughput biological big data from omics and other high-throughput approaches.

We consider all manuscripts related to machine learning, deep learning and statistical learning technique for unraveling biomedical big data, such as, those related to transcriptome, epigenome, single cell, genomics, drug repurposing, health informatics, imaging, medical informatics, etc.

We particularly welcome new methods, tools, databases, web servers, software and new data processing pipelines that are immediately available to the broad research community.

Different type of articles can be published within this special issue: Original Research and Mini-Review. Other types of articles can be proposed to the special issue Editors who will evaluate their suitability.

However, simple biomarker prediction & analysis based on mining public biological big database such as TCGA or ENCODE without in-depth experimental validation will NOT be considered.

Dr. Jia Meng and Dr. Wei Chen

Guest Editors