Dr Namshik Han

University of Cambridge

University institutes
Milner Therapeutics Institute

Position: Head of Computational Research & Artificial Intelligence
Personal home page: https://www.milner.cam.ac.uk/machinelearning/
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 research and AI 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.

Secondary Programme
Early Cancer Institute
Strategic Resources
The Milner Therapeutics Institute and Consortium
Methods and technologies
Computational modelling
DNA sequencing
Gene expression profiling
Public health
Statistical analysis
X-ray crystallography
Tumour type interests
Brain and central nervous system
Computational Biology
Machine Learning
Artificial Intelligence
Drug Discovery
Recent publications:
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Key publications

Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies
N Han*§, W Hwang*, Tzelepis K*, … F Weber and T Kouzarides§ (2021) Science Advances

Current and prospective computational approaches and challenges for developing COVID-19 vaccine
W Hwang*, W Lei*, NM Katritsis*, M MacMahon*, K Chapman and N Han (2021) Advanced Drug Delivery Reviews

Methylation of histone H3 at lysine-37 by Set1 and Set2 prevents spurious DNA replication
H Santos-Rosa*§, G Millan-Zambran*, N Han*, T Leonardi, M Klimontova, ... and T Kouzarides§ (2021) Molecular Cell

The inflammatory niche directs alveolar regeneration via Krt8+Cldn4+ regenerative progenitors
J Choi, J-E Park†, G Tsagkogeorga†, B-K Koo, N Han‡ and J-H Lee (2020) Cell Stem Cell

Differential expression of soluble receptor for advanced glycation end-products (sRAGE) in mice susceptible or resistant to chronic colitis
M Bramhall, K Rich, A Chakraborty, L Logunova, N Han†, ... and S Cruickshank (2020) Inflammatory Bowel Diseases

A novel long noncoding RNA Linc-ASEN represses cellular senescence through multileveled reduction of p21 expression
H C Lee*, D Kang*, N Han†, Y Lee, … and J Lee (2019) Cell Death & Differentiation

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

Long non-coding RNA ChRO1 facilitates ATRX/DAXX-dependent H3.3 deposition for transcription-associated heterochromatin reorganization
J Park, H Lee, N Han†, S Kwak, … and E J Cho (2018) Nucleic Acid Research

DDX3X RNA helicase affects breast cancer cell cycle progression by regulating expression of KLF4
E Cannizzaro*, A J Bannister*, N Han†, and T Kouzarides (2018) FEBS Letters

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*, … N Han†, … G Vassiliou§ and T Kouzarides§ (2017) Nature

FOXM1 and polo-like kinase 1 are co-ordinately overexpressed in patients with gastric adenocarcinomas
M Dibb, N Han†, J Choudhury, … and A D Sharrocks (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, … and A D Sharrocks (2015) Molecular Cancer

The forkhead transcription factor FOXK2 acts as a chromatin targeting factor for the BAP1-containing histone deubiquitinase complex
Z Ji, H Mohammed, … N Han, … and A D Sharrocks (2014) Nucleic acids research

The Forkhead TF FOXM1 Controls Cell Cycle-Dependent Gene Expression through an Atypical Chromatin Binding Mechanism
X Chen, G A Muller, … N Han, … and A D Sharrocks (2013) Molecular and Cellular Biology

Progressive lung cancer determined by expression profiling and transcriptional regulation
N Han*, Z Dol*, O Vasieva, … and J K Field (2012) International Journal of Oncology

The FOXM1-PLK1 axis is upregulated in oesophageal adenocarcinoma
M Dibb, N Han†, J Choudury, … and A D Sharrocks (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
† Bioinformatics lead author
‡ Bioinformatics supervisor

Combining mathematics and biology in computer science can produce novel insights in the 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 biological mechanism. Image courtesy of Namshik Han