Dr Namshik Han

University of Cambridge
Milner Therapeutics Institute

Position: Head of Computational Biology
Personal home page:
Email:   n.han@milner.cam.ac.uk

PubMed journal articles - click here

Dr Namshik Han is pleased to consider applications from prospective PhD students.

Research description

My role is to lead the computational biology group at the Milner Therapeutics Institute University of Cambridge. I have responsibility to develop and deliver the Milner’s computational biology and bioinformatics strategy. I lead implementation of large scale data analysis which will involve collation and analysis of data from a variety of sources including third-party data, in-house data and publicly generated datasets. The main aim of my group at the Milner is to facilitate access of partner organisations to cutting-edge bioinformatics technology and to develop new computational methods fulfilling the global mission of identifying new or better therapies from the analysis of biological data. This will contribute to the inter-disciplinary environment of the Milner, allowing dynamic translation and validation of findings from in silico to in vitro models, and from one therapeutic area to another. The main activity of the group is the development and application of machine learning, statistical and mathematical approaches to pharmacogenomics and drug discovery, strongly focusing on the use of publicly available big data, and utilising experimental data generated on purpose by the Milner Consortium partner organisations. Projects within the group include (1) identification of new therapeutic targets in a variety of diseases, (2) stratification of patients to improve personalised medicine, (3) prediction of efficacy and safety of new and existing drugs and (4) identification of drug positioning/repositioning opportunities.

Research Programme
Onco-Innovation
Methods and technologies
Bioinformatics
Computational modelling
DNA sequencing
Gene expression profiling
Genomics
Microarray
PCR
Statistical analysis
X-ray crystallography
Tumour type interests
Kidney
Leukemia
Liver
Lung
Prostate
Keywords
Computational Biology
Bioinformatics
Machine Learning
Drug Discovery
nh417
Recent publications:
 Retrieving latest data from feed...

Symplectic Elements feed provided by Research Information, University of Cambridge


Key publications

Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci
P Amaral*, T Leonardi*, N Han*, E Vire, D K Gascoigne, et al. (2018) Genome Biology

TIGERi: Modeling and visualizing the responses to perturbation of a transcription factor network
N Han§, H Noyes, A Brass§ (2017) BMC Bioinformatics

Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control
I Barbieri*, K Tzelepis*, L Pandolfini*, J Shi, S Robson, V Migliori, N Han, et al. (2017) Nature

FOXM1 and polo-like kinase 1 are co-ordinately overexpressed in patients with gastric adenocarcinomas
M Dibb, N Han, J Choudhury, et al. (2015) BMC research notes

Deregulation of the FOXM1 target gene network and its coregulatory partners in oesophageal adenocarcinoma
E F Wiseman, X Chen, N Han, A Webber, et al. (2015) Molecular Cancer

Progressive lung cancer determined by expression profiling and transcriptional regulation
N Han*, Z Dol*, O Vasieva, et al. (2012) International Journal of Oncology

The FOXM1-PLK1 axis is upregulated in oesophageal adenocarcinoma
M Dibb, N Han, J Choudury, et al. (2012) British Journal of Cancer

Protein kinase C regulates late cell cycle-dependent gene expression
Z Darieva, N Han, K Doris, B A Morgan, A D Sharrocks (2012) Molecular and Cellular Biology

* Co-first author
§ Co-corresponding author

Combining mathematics and biology in computer science can produce new biology. Computational approaches not only ensure implementation of highly elaborated mathematical model using large scale genome-wide datasets generated by the high-throughput experiments, but also practicably analyse and visualize the results from the mathematical modelling process. The computationally derived results provide comprehensive understanding of transcriptional regulatory mechanism. Image courtesy of Namshik Han