**Preface to "Bioinformatics Applications Based On Machine Learning"**

Research in the area of bioinformatics has always been one of the most active lines of research in the scientific community. However, it has recently gained even more interest thanks to advances in the information technology (IT) sector, including the increased processing capacities of computers, which allow processing large volumes of data and analyzing them with techniques such as machine learning.

Thanks to these advances, new applications appear in the area of bioinformatics. In them, the results obtained generally improve those of previous applications that do not use these computation techniques.

This book presents papers that have been accepted for the Special Issue "Bioinformatics Applications Based On Machine Learning" of the journal Processes, where authors were encouraged to submit their original research dealing with new machine learning algorithms, distributed machine learning systems, new applications in bioinformatics, healthcare applications, bioimaging, next-generation sequencing, data and software integration, visualization of biological systems and networks, high-throughput data analysis (transcriptomics, proteomics, etc.), comparison and alignment methods, and other related topics.

After several rounds of review, 10 research articles and 1 review (entitled "A Review of Computational Methods for Clustering Genes with Similar Biological Functions") were accepted for publication in the Special Issue and are included in this book.

The research articles include the use of a wide variety of IT techniques such as convolutional neural networks, gradient boosting, multilayer bi-directional LSTM, particle swarm optimization or harmony search, among others, applied to domains such as body part detection in images and video, diabetes, or the study of *Arabidopsis thaliana* or *Saccharomyces cerevisiae*, among others.

Finally, as the Guest Editors, we would like to take this opportunity to thank all the contributing authors and the reviewers for their hard and highly valuable work. In addition, we acknowledge the funding support for the project "Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Block-chain-based framework for IoT Security", Reference: RTI2018-095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER), and last but not least, the MDPI staff for their hard work, which was essential for the success of this book.

#### **Pablo Chamoso, Sara Rodr´ıguez Gonz´alez, Mohd Saberi Mohamad, Alfonso Gonz´alez-Briones** *Editors*

*Article*
