Kridsadakorn Chaichoompu

Dr Chaichoompu has scientific expertise in informatics (parallel/high-performance computing and machine learning), bioinformatics, and biostatistics. He has been working for multidisciplinary scientific research since he started his research career in 2006 as a Research Assistant at Biostatistics and Informatics Laboratory, Genome Institute, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand. During his time at BIOTEC, he was involved in many research projects which could be categorized into five main areas: genomic data analysis, population structure analysis, tool and algorithm development, web application and database development, and computer hardware acceleration.

In 2017, he obtained his PhD in Applied Science (Bioinformatics) from Université de Liège, Belgium. His PhD research was involved in methodology development to detect fine-scale population structure towards patient molecular reclassification. During his PhD study, he also gained experience in teaching and organizing conferences. Besides, he has been working with a molecular subgrouping working group and an epistasis working group of the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) since the early period of his PhD study.

Later, he became a post-doctoral researcher at Max Planck Institute of Psychiatry in Munich, Germany, until December 2018. His postdoctoral research was about methodology development for genetic analysis in patients using machine learning and deep learning. Currently, he is working as a post-doctoral researcher at Helmholtz Zentrum München since May 2019.

Posts by Kridsadakorn Chaichoompu

Western African inhabitants

The investigation of population structure permits 1) relegating people to distinct ethnic gatherings living together at a specific locale, 2) examining movements from the cause of admixed populaces, and 3) evaluating and portraying bewildering of shared hereditary parentage in affiliation contemplates.

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R Package KRIS

Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.

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