EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Atif Khan
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I have over 8 years of industry experience managing IT infrastructure and software. Before this, I completed my BEng in Electronics and Communication Engineering, Masters in Computer Network Systems and Masters in Cloud Computing.

I joined the CDT because of its unique ecosystem, collaborative work amongst students, and diverse industry partners. I aspire to conduct high impacting research in an interdisciplinary subject and become a leader in Data Science. The CDT is well placed to help me achieve this.

PhD Title

mitoML: Machine learning approaches to understand mitochondrial disease pathology

 “one-size-fits-all” approach in medicine is inefficient and sometime harmful . Application of AI for personalised medicine has potential to radically change every aspect of healthcare. And one such area is the AI driven approach to understand the disease pathology that can help with personalised diagnosis and prognosis. The personalised diagnoses and prognoses can greatly help in rare mitochondrial diseases where people suffer from a same disease present varied severity of symptoms.

Mitochondria are organelles that produce ~90% of the energy consumed within each of the trillions of cells that make up a human body. Mitochondria have their own genome: mtDNA coding for some mitochondrial proteins and the rest are coded in nDNA. Pathogenetic mutations in these genetic codes manifest into mitochondrial diseases.  In the Wellcome Centre for Mitochondrial Research, at Newcastle University have some of the best access to mitochondrial disease patient tissue in the world. And hence access to scarce clinical (including omics) data from these patients.  

In my PhD project mitoML, we use scarce single-cell omics and associated clinical data from patients of rare mitochondrial diseases, and machine learning techniques to understand pathology of these diseases. The high-level plan is to use novel multimodal machine learning techniques, and genomic, proteomic and other clinical phenotype data, to not just make non-obvious predictions but also discover underlining disease pathology.


Stephen McGough, Conor Lawless, Amy Vincent