Artificial Intelligence and Machine Learning at the Milner Therapeutics Institute

Artificial Intelligence and Machine Learning at the Milner Therapeutics Institute

The aim of our computational research group, led by Dr Namshik Han is to create bespoke machine learning methods to uncover novel disease mechanisms through the integration and interrogation of large multi-omic datasets. Our approaches have been applied to target identification and repositioning and are applicable across many areas of healthcare including early detection and personalised medicine.

We’d like to work with you. Please contact us using the button below.

The aim of our computational research group, led by Dr Namshik Han is to create bespoke machine learning methods to uncover novel disease mechanisms through the integration and interrogation of large multi-omic datasets. Our approaches have been applied to target identification and repositioning and are applicable across many areas of healthcare including early detection and personalised medicine.

We’d like to work with you. Please email us at machinelearning@milner.cam.ac.uk

Sharing expertise and partnering with industry to inform decision making in drug discovery
Using bespoke machine learning methods to identify new signatures of disease and therapeutic targets
Working with unique patient datasets and disease models
Using network analysis to gain a deep understanding of the underlying causes of disease

Discover our Research:

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Research Collaborators

Industry Collaborators



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Academic Collaborators

 




 




 









 




 

Funders

Research Collaborators

Industry Collaborators

GSK logo

Standigm logo

 

Academic Collaborators

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Group Members

Group Leader
Namshik Han (Head)

Senior Scientist
Georgia Tsagkogeorga

Postdoctoral Researchers
Sanjay Rathee
Gehad Youseff
Holly Giles

PhD Students
Nicholas Katritsis
Anika Liu
Winnie Lei
Greta Baltusyte
Sofia Radrizzani

Alumni
Noah Collins
Woochang Hwang
Seungbeom Lee
Méabh MacMahon
Sophia Ramani
Ming Zeng
Soorin Yim
Lilly Wollman
Mark Wong
Rebecca Marrow
Louise Lecointre

Selected Publications

S Yim, W Hwang, N Han§, D Lee§.
Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens.
Frontiers in Immunology (2022)

S Rathee*, M MacMahon*, A Liu, N Katritsis, G Youssef, W Hwang, L Wollman, N Han.
DILIC: An AI based classifier to search for Drug-Induced Liver Injury literature. 
Frontiers in Genetics (2022)

A Liu, N Han, J Munoz-Muriedas, A Bender.
Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI).
PLOS Computational Biology (2022)

N Katritsis§, A Liu, G Youssef, S Rathee, M MacMahon, W Hwang, L Wollman, N Han.
Dialogi: Utilising NLP with chemical and disease similarities to drive the identification of Drug-Induced Liver Injury literature. 
 
Frontiers in Genetics (2022)

W Hwang and N Han.
Identification of potential pan-coronavirus therapies using a computational drug repurposing platform.
Methods (2021)  

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

Namshik Han*§, Woochang Hwang*, Konstantinos Tzelepis*, Patrick Schmerer*, Eliza Yankova, Méabh MacMahon, Winnie Lei, Nicholas M Katritsis, Anika Liu, Alison Schuldt, Rebecca Harris, Kathryn Chapman, Frank McCaughan, Friedemann Weber, Tony Kouzarides§
Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies.
Science Advances (2021)

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

P Amaral*, T Leonardi*, N Han*, E Vire, D Gascoigne, R Arias-Carrasco, M Büscher, L Pandolfini, Anda Zhang S Pluchino, V Maracaja-Coutinho, H I. Nakaya, M Hemberg, RShiekhattar, A J. Enright and T Kouzarides.
Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci.
Genome Biology (2018)

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

* Co-first author   § Co-corresponding author

Selected Publications

S Yim, W Hwang, N Han§, D Lee§.
Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens.
Frontiers in Immunology (2022)

S Rathee*, M MacMahon*, A Liu, N Katritsis, G Youssef, W Hwang, L Wollman, N Han.
DILIC: An AI based classifier to search for Drug-Induced Liver Injury literature. 
Frontiers in Genetics (2022)

A Liu, N Han, J Munoz-Muriedas, A Bender.
Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI).
PLOS Computational Biology (2022)

N Katritsis§, A Liu, G Youssef, S Rathee, M MacMahon, W Hwang, L Wollman, N Han.
Dialogi: Utilising NLP with chemical and disease similarities to drive the identification of Drug-Induced Liver Injury literature. 
 
Frontiers in Genetics (2022)

W Hwang and N Han.
Identification of potential pan-coronavirus therapies using a computational drug repurposing platform.
Methods (2021)  

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

Namshik Han*§, Woochang Hwang*, Konstantinos Tzelepis*, Patrick Schmerer*, Eliza Yankova, Méabh MacMahon, Winnie Lei, Nicholas M Katritsis, Anika Liu, Alison Schuldt, Rebecca Harris, Kathryn Chapman, Frank McCaughan, Friedemann Weber, Tony Kouzarides§
Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies.
Science Advances (2021)

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

P Amaral*, T Leonardi*, N Han*, E Vire, D Gascoigne, R Arias-Carrasco, M Büscher, L Pandolfini, Anda Zhang S Pluchino, V Maracaja-Coutinho, H I. Nakaya, M Hemberg, RShiekhattar, A J. Enright and T Kouzarides.
Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci.
Genome Biology (2018)

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

* Co-first author   § Co-corresponding author

Case Study: LifeArc

 Milner-LifeArc collaboration for novel disease signatures in oncology and infectious disease

The Milner Computational Research team, led by Namshik Han, have been collaborating closely with the medical research charity LifeArc over the last 3 years to develop and apply bioinformatics tools to identify novel therapeutic targets, biomarkers and drug repositioning opportunities.

By applying cutting-edge computational methods and analysis of multi-modal biomedical datasets, the team have identified new or better drug targets across a range of therapeutic areas. Key areas of research include the development and application of artificial intelligence, machine learning, statistical and mathematical approaches to pharmacogenomics and drug discovery.

Oncology research. LifeArc brings a rich oncology drug discovery programme to this collaboration. By developing methods to integrate LifeArc proprietary datasets with public cancer databases, the team have created a bespoke AI platform and computational pipeline to reveal novel disease signatures and then conducted agnostic pathway analysis to uncover new targets. Key outcomes from this work have included identification of new disease signatures for six cancer types in the LifeArc programme, and repositioning of an existing LifeArc target as a potential therapeutic strategy for an unanticipated cancer type.

COVID research. With the support of LifeArc, the group have also applied their bespoke AI methods and network analysis to uncover hidden pathways and reposition 200 already approved drugs against COVID-19. 20% of these drugs are currently in COVID-19 clinical trials, and the group went on to successfully validate two drugs in cellular assays (Proguanil and Sulfasalazine). These SARS-CoV-2 induced pathways define a resource for repurposing of drugs against COVID-19, either as monotherapies or in combination therapy.

“This collaboration has benefited enormously from the expertise of the LifeArc team in drug discovery. We see great potential to now build on the database resources and pipelines we have created together and apply them to reveal new disease signatures in oncology and other areas with high unmet medical need” says Namshik.

Dr David Pardoe, Head of Technology Development at LifeArc said:

“It is essential that we apply the power of massive and complex data to better understand fundamental biology and disease pathways. Doing this through the integration of different scientific disciplines represents just one of the ways we can collaboratively create real impact for patients”.

Case Study: Storm Therapeutics

Predicting novel human RNA methyltransferases using machine learning

The Milner Computational Research Group have been working with Storm Therapeutics for the past two years to apply machine learning techniques for prediction of novel human RNA methyltransferases.

Storm Therapeutics, co-founded by our Director Tony Kouzarides and Eric Miska (Gurdon Institute) is a Cambridge-based biotech company targeting RNA-modifying enzymes, including RNA methyltransferases, in the treatment of cancer.

RNA methyltransferases are known to functionally regulate RNAs, and have attracted increasing interest as potential drug targets given their newly identified role in cancer. There are many methylation sites known to exist on RNA, but enzymes have not been ascribed for many of them, suggesting that there are a number of yet to be identified RNA methyltransferases. Georgia Tsagkogeorga, a senior scientist at Storm, set out to identify new enzymes in this family with the Milner group, using the power of machine learning. By collecting and collating transcriptomic, proteomic, structural and protein-protein interaction data for human RMTs into a harmonized database, she built a large novel machine learning dataset. She has now applied four supervised machine learning models to this database, each of which produced novel predictions of RMTs. By consolidating the model outcomes to predict novel RMT genes, the team have now selected putative novel RMTs for further validation.

“This approach is demonstrating the strong potential of machine learning to interrogate large scale databases consisting of integrated multi-omics datasets and thereby make unanticipated predictions about this enigmatic enzyme family. We are excited about the potential of this partnership to identify new epigenetic regulators with therapeutic significance” says our Director Tony Kouzarides.