Application Of Omics, Ai And Blockchain In Bioinformatics Research

by Jeffrey J P Tsai, Ka-lok Ng

Overview

With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level data problems in bioinformatics and biomedical research in this post-genomics era, computational biology has been playing an increasingly important role in paving the way as basis for patient-centric healthcare.Two such areas are: (i) implementing AI algorithms supported by biomedical data would deliver significant benefits/improvements towards the goals of precision medicine (ii) blockchain technology will enable medical doctors to securely and privately build personal healthcare records, and identify the right therapeutic treatments and predict the progression of the diseases.A follow-up in the publication of our book Computation Methods with Applications in Bioinformatics Analysis (2017), topics in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug discovery, RNA-seq analysis, tensor decomposition and Boolean network.

Product Details

ISBN-13:	9789811203572
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	10/24/2019
Series:	Advanced Series In Electrical And Computer Engineering , #21
Pages:	208
Product dimensions:	6.00(w) x 9.00(h) x 0.50(d)

Table of Contents

Generalized Iterative Modeling for Clinical Omics Data Analysis (Kung-Hao Liang); Explainable AI: Mining of Genotype Data Identifies Complex Disease Pathways — Autism Case Studies (Matt Spencer, Saad Khan, Zohreh Talebizadeh and Chi-Ren Shyu); Blockchain for Pre-Clinical and Clinical Platform with Big Data Analytics (Yin-Wu Chen and Zonyin Shae); Analysis of Circulating Tumor DNA in Patients with Cancer, a Clinical Perspective (Chi-Chun, Yeh and Peter Mu-Hsin Chang); Big Data Computation of Drug Design: From the Natural Products to the Transcriptomic-Based Molecular Development (David Agustriawan, Arli Aditya Parikesit, Rizky Nurdiansyah); A Hybrid Approach Integrating Model-Based Method and Gene Functional Similarity for Cluster Analysis of RNA-Seq Data (Ming-Han Chan, Pin-Chen Chou, Rong-Ming Chen and Rouh-Mei Hu); High-Performance Computing for Measurement of Cancer Gene Signatures (Hsueh-Ting Chu); High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Data Processing (Li Chuang and Lin Feng); Analysis of Boolean Networks and Boolean Models of Metabolic Networks (Tatsuya Akutsu);