WORKSHOPS
WORKSHOP 1
LIM KIAN MING
Dr Lim Kian Ming received B.IT (Hons) in Information Systems Engineering, Master of Engineering Science (MEngSc), and Ph.D. (I.T.) degrees from Multimedia University. His Ph.D. dissertation emphasizes on machine learning for pattern recognition. He has published a number of influential publications on Machine Learning, Computer Vision, and Pattern Recognition. His research works have also qualified him to receive several innovation awards at the University, National, and International level. He is currently a lecturer with the Faculty of Information Science and Technology, Multimedia University. His research interests include machine learning, computer vision, and pattern recognition.
Deep Learning for Medical Image Analysis
Deep learning is a subset of machine learning methods based on artificial neural networks with representation learning. It is an algorithm that is inspired by how the human brain processes the data and performs learning from large amounts of data. Inspired by the successes of deep learning in computer vision, researchers have applied it in medical image analysis. In this workshop, a deep neural network will be introduced to perform learning and classification on the chest x-ray images.
Prerequisites:
- Software: Google Colaboratory
- Skills: Basic knowledge in Python
WORKSHOP 2
GAVIN WEE WEI YEE
Dr Wei Yee Wee received his B.Sc. in Bioinformatics from Multimedia University (MMU) in 2012. After graduation, he continued with his Ph.D in Bioinformatics in University of Malaya and continued to work as Post Doctoral Research Fellow under Centre for Research in Biotechnology for Agriculture (CEBAR), University of Malaya after he gained his Ph.D. He has gained many years of experience in the field of bioinformatics research. Currently, he is a lecturer in the School of Sciences and engages in education and research through contribution to multidisciplinary research projects, research funding and research publication in ISI Journals. He has a proven record in publication and published around 24 high impact papers since 2012. He has also taken part in setup, design, establishment and maintenance of a new bioinformatics laboratory.
Sequence Mapping and Variant Calling Analysis
Next-generation sequencing is the standard method to produce genomic and transcriptomic data and knowledge about an organism. Sequencing produces a collection of sequences without genomic context and raw data. The raw data (Fastq files) generated from the sequencing remain unknown to which part of the genome the sequences correspond to. Thus, mapping raw reads to a reference genome is a key step in modern genomic data analysis. Variant calling is one of the Bioinformatics analyses that can be performed after the sequence mapping.
Variant calling is the process of detecting the differences between an aligned sequenced genome and a reference genome. In other words, “variants” do not exist per se and only are defined in comparison to another genome. Variant analysis is a crucial procedure for whole exome, targeted panels, and whole genome sequencing.
Prerequisites:
- Software: Virtual Machine
*A manual will be provided