Hello, this is Mohan Timilsina. I am a postdoctoral researcher at the Data Science Institute @ National University of Ireland Galway. I am interested in how entities can interact and how they can be modeled in terms of networks. From last few years, I am investigating how diffusion based models applied in the network such as biological networks of protein-protein interaction, document networks of web pages to heterogeneous networks such as linked data (RDF graph) can solve the problem of link prediction and node classification. I completed my PhD in Computer Science from Data Science Institute @ National University of Ireland Galway, MSc in Informatics from the Asian Institute of Technology Thailand, under the field of study Computer Science and Information Management (CSIM) and Bachelors in Information Management (BIM) from Tribhuvan University Nepal.
My research interest includes:
1. Applied Machine learning
2. Network Analysis and graph theory.
3. Simulation and Modelling
4. Information Visualization.
5. Web Application Engineering
Courses I am Teaching Assistant:
- ST236 : Statistical Inference (School of Mathematics, Statistics and Applied Mathematics)
- ST313 : Applied Regression Models (School of Mathematics, Statistics and Applied Mathematics)
- CT2106: Object-Oriented Programming using JAVA (Computer Science and Information Technology)
Recently Accepted Papers:
- "Integration of Medical and Genomic Information to Enhance Relapse Prediction in Early Stage Lung Cancer Patients". Mohan Timilsina, Dirk Fey, Adrianna Janik, Maria Torrente, Mariano Provencio, Alberto Cruz Bermúdez, Enric Carcereny, Luca Costabello, Delvys Rodríguez Abreu, Manuel Cobo, Rafael López Castro, Reyes Bernabé, Dra. Maria Guirado, Pasquale Minervini, Vit Novacek. [Venue: American Medical Informatics Association 2022, Annual Symposium (Conference)]
- "Machine learning approaches for predicting the onset time of the adverse drug events in oncology", Mohan Timilsina, Meera Tandan, Vit Novacek. [Venue: Machine Learning With Application (Journal)]
Journal Publications:
- What identifies different age cohorts in Yahoo! Answers? Knowledge-Based Systems (2021): 107278. IF: 8.038
- Semi-supervised regression using diffusion on graphs. Applied Soft Computing (2021): 107188. IF: 6.72
- Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method. Nature Scientific Reports. IF 4.122
- Synergy Between Embedding and Protein Functional Association Networks for Drug Label Prediction using Harmonic Function. IEEE/ACM Transactions on Computational Biology and Bioinformatics. IF: 3.01
- Predicting Links between Tumor Samples and Genes using 2-Layered Graph Based Diffusion Approach. BMC-Bioinformatics IF 3.24
- Discovering Symptom Patterns of COVID-19 Patients Using Association Rule Mining. Computers in Biology and Medicine IF 3.43
- Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: a retrospective cohort study. International Journal of Medical Informatics. IF: 3.210
- Heat Diffusion Approach for Scientific Impact Analysis in Social Media. Social Network Analysis and Mining (2019).
- Social Impact Assessment of Scientist from Mainstream News and Weblogs. Social Network Analysis and Mining (2017) 7: 48. Volume 7, Issue 1.
Conference Publications:
- A Diffusion-Based Method for Entity Search. 2019 IEEE 13th International Conference on Semantic Computing (ICSC), California, USA.
- A 2-Layered Graph Based Diffusion Approach for Altmetric Analysis. The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2018 Barcelona, Spain.
- Predicting Citations from Mainstream News, Weblogs and Discussion Forums. Proceedings of the International Conference on Web Intelligence Leipzig, Germany — August 23 - 26, 2017 .
- Towards predicting academic impact from mainstream news and weblogs: A heterogeneous graph based approach. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016) USA.
- Spatial Explicit Model to Visualize the Spread of Epidemic Disease in a Network. Proceedings of the International Workshops SOCNET 2014 , Germany.
Poster Presentation:
- “Link Prediction in a Multi Relational Graph: A case study of cancer methylation data” . Mohan Timilsina, Ratnesh Sahay and Dietrich Rebholz Schumann 7th Postgraduate research day 2017 (Poster Presentation) - National University of Ireland, Galway