# Artificial Intelligence and Data Science for Health

Developing novel statistical machine learning techniques to improve health and wellbeing through collaboration with the biological and medical sciences

# What we do

Our research seeks to develop novel statistical and machine learning methodologies by working in partnership with the biological and medical sciences. Inspired by real applications, we are creating artificial intelligence technologies that can make a real impact on improving human health.

# Meet the Team

## Christopher Yau

### Professor of Artificial Intelligence

Statistical Machine Learning, Computational Genomics, Digital Health

## Dominic Danks

Statistical Machine Learning

## Yun Feng

Statistical Machine Learning, Cancer Genomics

## Franziska Gunther

Psychology, Digital Health

## Kaspar Maertens

Statistical Machine Learning

## Woojung Kim

Bayesian Statistics, Machine Learning

## Hrvoje Misetic

Bioinformatics, Cancer Genomics

## Anas Rana

### Research Fellow

Statistical Machine Learning, Cancer Genomics

## Zhiyuan Hu

### Postdoctoral Researcher

Cancer Genomics, Ovarian Cancer

## Joel Nulsen

Bioinformatics, Cancer Genomics

## Kieran Campbell

Statistical Machine Learning, Genomics

## Constantin Ahlmann-Eltze

Statistical Machine Learning

## Donatien Chedom-Fotso

### Postdoc

Statistical Machine Learning, Cancer Genomics

## Paul Kirk

### Postdoctoral Research Fellow

Statistic modelling, Approximate Inference, Genomics

## Leon Law

Statistical Machine Learning, Kernel Methods

## Emma Pierson

Statistical Machine Learning

## Tammo Rukat

Statistical Machine Learning

## Siyu Chen

Statistical Machine Learning, Bioinformatics

## Xiaole Zhang

### Alumni

Monte Carlo Methods, Approximate Inference

## Justina Zurauskiene

### Postdoctoral Researcher

Statistical Machine Learning, Genomics

# Projects

#### BIRM-CAM

Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity (BIRM-CAM) project between the Universities of …

#### Breaking Free with Artificial Intelligence

The Data Science of Digital Behaviour Intervention

#### Convolutional filtering for mutation signatures

A novel convolutional filter-based approach for learning mutation signature representations.

#### Health Data Research UK-Turing PhD Programme

To develop future leaders in health data science.

#### Multi-Omics Network inference

A novel Bayesian nonparametric regression approach for high-dimensionality multi-modal data integration.

#### Neural Decomposition

Developing interpretable deep neural network approaches for dimensionality reduction that embed functional ANOVA decompositions within …

#### Precision medicine and individual-level cancer driver genes

Identification of cancer-promoting genetic alterations using genome sequencing and machine learning.

#### RLevolution

Unravelling the history of genomic instability through deep reinforcement learning.

#### The Automatic BioData Scientist

Developing automated learning frameworks for reproducible and transferable biological data analysis.

#### Ovarian Cancer

Computational genomics to tackle the silent killer

# Recent Posts

### New eLife publication: A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes

Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old …

### PhD Opportunities 2020/21

There are two new funded PhD opportunities for UK/EU students available to join my group for the academic year 2020/21: Explainable …

# Recent Publications

Quickly discover relevant content by filtering publications.

### The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells

We used single cell sequencing to map the repertoire of cell types in the fallopian tube epithelium of ovarian cancer patients. We …

### Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

We proposed a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent …

### Deep single-cell RNA-seq of the putative cell of origin revealed a novel molecular subtype of high-grade serous ovarian cancer with poor prognosis

We are a decentralised group of researchers operating across institutional or disciplinary barriers. We do science to truly benefit the greater good.

We are looking for researchers who buy into this philosophy to become involved with our research activities. You can join as directly as a PhD student or postdoctoral researcher or, alternatively, you can collaborate on a less formal basis as an Affiliate or Visitor.

## PhD

Admissions are currently being taken through the following study programmes at the University of Manchester, please see the websites for more details:

Potential applicants are encouraged to contact the Principal Investigator before making applications.

## Affiliates / Visitors

If your study programme involves external laboratory placements then we would be very interested to see how we can host and support a visit to our group. We have welcomed numerous visitors from across the world over the years. Contact the Principal Investigator for further information.

The Alan Turing Institute provides an Enrichment programme that allows students to take up to a year of their PhD to be based in London at the Turing Institute. If you would like to apply for the Enrichment scheme and to work with the group during the placement then contact the Principal Investigator.

# Contact

• Professor Christopher Yau, Michael Smith Building, University of Manchester, Dover St, Manchester, M13 9PT
• DM Me