This thorough volume provides an in-depth introduction to and discussion of microRNAs (miRs) and their targets, miR functions, and computational techniques applied in miR research, thus serving the need for a comprehensive book focusing on miR target genes, miR regulation mechanisms, miR functions performed in various human diseases, and miR databases/knowledgebases. Without prior knowledge of the area of study, computational biologists, computer scientists, bioinformaticians, bench biologists, as well as clinical investigators will find it easy to follow the techniques in this collection. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that ensures successful results. Accessible and practical, Bioinformatics in MicroRNA Research functions as an ideal guide for researchers of all backgrounds to explore this vital area of study.

This thorough volume provides an in-depth introduction to and discussion of microRNAs (miRs) and their targets, miR functions, and computational techniques applied in miR research, thus serving the need for a comprehensive book focusing on miR target genes, miR regulation mechanisms, miR functions performed in various human diseases, and miR databases/knowledgebases. Without prior knowledge of the area of study, computational biologists, computer scientists, bioinformaticians, bench biologists, as well as clinical investigators will find it easy to follow the techniques in this collection. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that ensures successful results. Accessible and practical, Bioinformatics in MicroRNA Research functions as an ideal guide for researchers of all backgrounds to explore this vital area of study.

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.