Understanding disease progression and comorbidities using multi-omics and clinical information

Summary

This research project aims at exploring clinical and multi-omics data to improve our present understanding of disease progression, transition and comorbidities. To achieve this aim, this project will use techniques, methods and models from Big data analytics, Complex networks and Machine learning.

Supervisor(s)

Dr Shahadat Uddin

Research Location

Research Cluster on Complex Systems

Program Type

PHD

Synopsis

Many of the diseases do not develop alone in the human body. Instead, they lead to the development of other diseases in the long term. This negatively affects many quality measures of human life, including cost and life expectancy. Understanding disease progression, associated transitions and comorbidities can help healthcare policymakers and related stakeholders to take appropriate preventive actions, which will eventually reduce treatment cost and improve quality of life. On the other side, disease information regarding multi-omics and clinical information has recently gained wide attention for research investigation purposes. Using techniques, methods and models from Big data analytics, Complex networks and Machine learning, this project will build diseasome and comorbidity maps to have a better understanding of disease progression and transition.

Additional Information

  • Use of research technique / methodology / technology
           Big data, Complex network methods and Machine learning
  • Eligibility criteria / candidate profile
           Need to have a good background in data analytics and computer programming

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Keywords

Disease progression; Disease transition; Big data analytics; Diseasome and Comorbidities

Opportunity ID

The opportunity ID for this research opportunity is: 2469

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