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Mourad Elloumi - Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications

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Mourad Elloumi Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications
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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 readers 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.

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Mourad Elloumi - Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications
Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications
Mourad Elloumi
Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications

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Book cover of Deep Learning for Biomedical Data Analysis Editor Mourad - photo 1
Book cover of Deep Learning for Biomedical Data Analysis
Editor
Mourad Elloumi
Deep Learning for Biomedical Data Analysis
Techniques, Approaches, and Applications
1st ed. 2021
Logo of the publisher Editor Mourad Elloumi Computing and Information - photo 2
Logo of the publisher
Editor
Mourad Elloumi
Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia
ISBN 978-3-030-71675-2 e-ISBN 978-3-030-71676-9
https://doi.org/10.1007/978-3-030-71676-9
Springer Nature Switzerland AG 2021
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG

The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents
Part I Deep Learning for Biomedical Data Analysis
Samson Anosh Babu Parisapogu , Chandra Sekhara Rao Annavarapu and and Mourad Elloumi
Domenico Amato , Mattia Antonino Di Gangi , Antonino Fiannaca , Laura La Paglia , Massimo La Rosa , Giosu Lo Bosco , Riccardo Rizzo and Alfonso Urso
Ahmet Paker and Hasan Oul
Mirto Musci and Marco Piastra
Part II Deep Learning for Biomedical Image Analysis
Jitesh Pradhan , Arup Kumar Pal and Haider Banka
Ashif Sheikh , Jitesh Pradhan , Arpit Dhuriya and and Arup Kumar Pal
Cdric Wemmert , Jonathan Weber , Friedrich Feuerhake and and Germain Forestier
Ryad Zemouri and Daniel Racoceanu
Daniel A. Greenfield , Germn Gonzlez and Conor L. Evans
Part III Deep Learning for Medical Diagnostics
Ebenezer Jangam , Chandra Sekhara Rao Annavarapu and and Mourad Elloumi
Abedalrhman Alkhateeb , Ashraf Abou Tabl and Luis Rueda
Hasan Zafari , Leanne Kosowan , Jason T. Lam , William Peeler , Mohammad Gasmallah , Farhana Zulkernine and Alexander Singer
Asim Waqas , Dimah Dera , Ghulam Rasool , Nidhal Carla Bouaynaya and and Hassan M. Fathallah-Shaykh
Part I Deep Learning for Biomedical DataAnalysis
Springer Nature Switzerland AG 2021
M. Elloumi (ed.) Deep Learning for Biomedical Data Analysis https://doi.org/10.1007/978-3-030-71676-9_1
1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data
Samson Anosh Babu Parisapogu
(1)
Department of Computer Science and Engineering, Indian Institute of Technology, Dhanbad, Jharkhand, India
(2)
Faculty of Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia
Samson Anosh Babu Parisapogu
Email:
Chandra Sekhara Rao Annavarapu (Corresponding author)
Email:
Abstract

In the field of bioinformatics, the development of computational methods has drawn significant interest in predicting clinical outcomes of biological data, which has a large number of features. DNA microarray technology is an approach to monitor the expression levels of sizable genes simultaneously. Microarray gene expression data is more useful for predicting and understanding various diseases such as cancer. Most of the microarray data are believed to be high dimensional, redundant, and noisy. In recent years, deep learning has become a research topic in the field of Machine Learning (ML) that achieves remarkable results in learning high-level latent features within identical samples. This chapter discusses various filter techniques which reduce the high dimensionality of microarray data and different deep learning classification techniques such as 2-Dimensional Convolution Neural Network (2D- CNN) and 1-Dimensional CNN (1D-CNN). The proposed method used the fisher criterion and 1D-CNN techniques for microarray cancer samples prediction.

Keywords
Gene expression data Deep learning Convolution neural network Machine learning Classification
Introduction

Computational molecular biology is an interdisciplinary subject that includes different fields as biological science, statistics, mathematics, information technology, physics, chemistry and computer science. The analysis of biological data involves the study of a wide range of data generated in biology. This biological data is generated from different sources, including laboratory experiments, medical records, etc. Different types of biological data include nucleotide sequences, gene expression data, macromolecular 3D structure, metabolic pathways, protein sequences, protein patterns or motifs and medical images []. Unlike a genome, which provides only static sequence information, microarray experiments produce gene expression patterns that provide cell functions dynamic information. Understanding the biological intercellular and intra-cellular processes underlying many diseases is essential for improving the sample classification for diagnostic and prognostic purposes and patient treatments.

Biomedical specialists are attempting to find relationships among genes and disease or formative stages, as well as relationships between genes. For example, an application of microarrays is the revelation of novel biomarkers for cancer, which can give increasingly exact determination and monitoring tools for early recognition of a specific subtype of disease or assessment of the viability of a particular treatment protocol. Different technologies are used to interpret these biological data. For example, microarray technology is useful for measuring the expression levels of a large number of genes under different environmental conditions, and Next Generation Sequencing (NGS) Technology for massively parallel DNA sequencing. This kind of experiments on a large amount of biological data leads to an absolute requirement of collection, storage and computational analysis [].

In the last decade, biological data analytics has improved with the development of associated techniques such as Machine Learning (ML), Evolutionary Algorithms
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