Narjes Rohani

Thesis title: Data-driven insights into Precision Medicine training

Biography

Background

I am graduated with Master of Computer Science from Shahid Beheshti University, Iran. During my master's time, I accomplished several Bioinformatics research projects by proposing computational methods for biological problems. I have presented two methods for drug-drug interaction prediction based on neural networks as well as matrix factorization. My master's thesis on cancer molecular subtypes has been applied a deep embedded clustering approach to the molecular data to find meaningful subtypes for breast cancer. 

After that, I joined Princess Margaret Bioinformatics and Computational Genomics Laboratory as a volunteer where I worked on drug response prediction for cell lines.

I am now a PhD student of MRC funded Precision Medicine Doctoral Training Programme at University of Edinburgh, and my project aims to provide insight into learning precision medicine concepts. In other words, the project is decision support for designing a more practical and efficient approach for teaching health-related materials to students in such a way that improves their learning outcome.

Qualifications

  • Master of Computer Science with focus on bioinformatics - Shahid Beheshti University, Iran.
  • Bachelor of Computer Engineering - Technical and Vocational University, Iran.

Research summary

  • Computational modelling
  • Data analysis
  • Machine learning & Deep learning
  • Cancer informatics
  • Drug response prediction
  • Single-cell analysis
  • Medical education

Papers delivered

Rohani , N , Gal , K , Gallagher , M & Manataki , A 2023 , Early prediction of student performance in a health data science MOOC, Proceedings of the 16th international conference on educational data mining (EDM 2023) . International Educational Data Mining Society , Online , pp. 325–333 , International Conference on Educational Data Mining (EDM 2023) , Bengaluru , India , 11/07/23 . https://doi.org/10.5281/zenodo.8115721

Rohani, N., Gal, K., Gallagher, M., Manataki, A. (2023). Discovering Students’ Learning Strategies in a Visual Programming MOOC Through Process Mining Techniques. Process Mining Workshops. ICPM 2022. Lecture Notes in Business Information Processing, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-27815-0_39

 

Rohani N, Moughari FA, Eslahchi C. Discovering potential candidates of RNAi-based therapy for COVID-19 using computational methods. PeerJ. https://doi.org/10.7717/peerj.10505

Rohani N and Eslahchi C (2020) Classifying Breast Cancer Molecular Subtypes by Using Deep Clustering Approach. Front. Genet. 11:553587. https://doi.org/10.3389/fgene.2020.553587

Rohani, N., Eslahchi, C. & Katanforoush, A. ISCMF: Integrated similarity-constrained matrix factorization for drug–drug interaction prediction. Netw Model Anal Health Inform Bioinforma 9, 11 (2020). https://doi.org/10.1007/s13721-019-0215-3

Rohani, N., Eslahchi, C. Drug-Drug Interaction Prediction by Neural Network Using Integrated Similarity. Sci Rep 9, 13645 (2019). https://doi.org/10.1038/s41598-019-50121-3

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