This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. 
The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.

1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data

Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues
A Deep Learning Model for MicroRNA-Target Binding
Recurrent Neural Networks Architectures for Accidental Fall Detection on Wearable Embedded Devices
Medical Image Retrieval System using Deep Learning Techniques
Medical Image Fusion using Deep Learning
Deep Learning for Histopathological Image Analysis
Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualization
Convolutional Neural Networks in Advanced Biomedical Imaging Applications
Deep Learning for Lung Disease Detection from Chest X-Rays Images
Deep Learning in Multi-Omics Data Integration in Cancer Diagnostic
Using Deep Learning with Canadian Primary Care Data for Disease Diagnosis
Brain Tumor Segmentation and Surveillance with Deep Artificial Neural Networks.