Journal: Combinatorial Chemistry & High Throughput Screening
Guest editor(s):Dr. Yinglei Song
Co-Guest Editor(s): Junfeng Qu

Introduction

Recently, a large number of technologies based on artificial intelligence have been developed and applied to solve a diverse range of problems in the areas of biomedical, biochemical and bioinformatics problems. By utilizing powerful computing resources and massive amounts of data, methods based on artificial intelligence can significantly improve the reliability and performance of many devices and platforms for biomedical, biochemical and bioinformatics applications. Techniques including machine learning, deep neural networks, artificial intelligence, optimization algorithms, image processing and computer vision, and data mining have constituted the most important parts of machine intelligence. However, the success of artificial intelligence in a large number of practical areas also suggest the need to combine methods from different fields in artificial intelligence to provide solutions to more complex and interdisciplinary problems related to the areas of biomedical, biochemical and bioinformatics. A diverse set of challenges must be tackled to bring artificial intelligence to today’s emerging edge applications. For example, the mechanism processes of many diseases need to be accurately analyzed with methods based on artificial intelligence. New and cost-effective drugs can be designed and validated based on such methods. Gene therapy usually requires the accurate analysis of protein targets, the identification of cancer types and data evaluation. Interdisciplinary techniques that combine knowledge from a few different fields have started to emerge in state-of-the-art solutions for biomedical, biochemical and bioinformatics problems. The major goal of this special issue is thus to disseminate research results and promote research activities at the cutting edge of our knowledge regarding the new interdisciplinary developments in both theories and applications of artificial intelligence for biomedical, biochemical and bioinformatics applications.

Keywords

artificial intelligence methods, biochemical and bioinformatics applications, machine learning, optimization algorithms, biomedical image processing, data mining

Sub-topics

Subtopics include but are not limited to the following: