Mohan Timilsina

727 total citations
34 papers, 433 citations indexed

About

Mohan Timilsina is a scholar working on Artificial Intelligence, Information Systems and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Mohan Timilsina has authored 34 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 9 papers in Information Systems and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Mohan Timilsina's work include Complex Network Analysis Techniques (6 papers), Lung Cancer Treatments and Mutations (6 papers) and Expert finding and Q&A systems (5 papers). Mohan Timilsina is often cited by papers focused on Complex Network Analysis Techniques (6 papers), Lung Cancer Treatments and Mutations (6 papers) and Expert finding and Q&A systems (5 papers). Mohan Timilsina collaborates with scholars based in Ireland, Czechia and Spain. Mohan Timilsina's co-authors include Martín Serrano, Radhya Sahal, Meera Tandan, Haixuan Yang, Yogesh Acharya, Mathieu d’Aquin, Alejandro Figueroa, Conor Hayes, Brian Davis and Dietrich Rebholz‐Schuhmann and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Mohan Timilsina

32 papers receiving 423 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohan Timilsina Ireland 11 227 83 72 40 39 34 433
Matthew Michelson United States 10 350 1.5× 70 0.8× 274 3.8× 30 0.8× 40 1.0× 30 601
Grigorios Loukides United Kingdom 18 780 3.4× 38 0.5× 132 1.8× 30 0.8× 50 1.3× 59 1.0k
A. Razia Sulthana India 10 214 0.9× 75 0.9× 95 1.3× 32 0.8× 7 0.2× 26 405
Yenny Villuendas-Rey Mexico 12 281 1.2× 28 0.3× 108 1.5× 11 0.3× 30 0.8× 76 540
Houping Xiao United States 17 730 3.2× 91 1.1× 131 1.8× 44 1.1× 49 1.3× 37 1.0k
Michael R. Smith United States 9 291 1.3× 30 0.4× 55 0.8× 17 0.4× 19 0.5× 25 495
Matthew Herland United States 7 281 1.2× 28 0.3× 62 0.9× 15 0.4× 20 0.5× 9 453
Subhash Bhalla Japan 9 105 0.5× 144 1.7× 118 1.6× 27 0.7× 30 0.8× 84 360
A. Carlisle Scott United States 7 341 1.5× 53 0.6× 113 1.6× 17 0.4× 66 1.7× 11 570

Countries citing papers authored by Mohan Timilsina

Since Specialization
Citations

This map shows the geographic impact of Mohan Timilsina's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohan Timilsina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohan Timilsina more than expected).

Fields of papers citing papers by Mohan Timilsina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohan Timilsina. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohan Timilsina. The network helps show where Mohan Timilsina may publish in the future.

Co-authorship network of co-authors of Mohan Timilsina

This figure shows the co-authorship network connecting the top 25 collaborators of Mohan Timilsina. A scholar is included among the top collaborators of Mohan Timilsina based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohan Timilsina. Mohan Timilsina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Al-Qatf, Majjed, Saeed Hamood Alsamhi, Muhammad Asif Razzaq, et al.. (2025). RAG4DS: Retrieval-Augmented Generation for Data Spaces—A Unified Lifecycle, Challenges, and Opportunities. IEEE Access. 13. 39510–39522. 1 indexed citations
2.
Timilsina, Mohan, María Torrente, Mariano Provencio, et al.. (2025). Large language model vs. traditional machine learning: Evaluating predictive models for early detection of tumor relapse. Expert Systems with Applications. 283. 127641–127641. 1 indexed citations
3.
4.
Figueroa, Alejandro & Mohan Timilsina. (2024). Textual Pre-Trained Models for Age Screening Across Community Question-Answering. IEEE Access. 12. 30030–30038. 1 indexed citations
5.
Alsamhi, Saeed Hamood, Ammar Hawbani, Santosh Kumar, et al.. (2024). Empowering Dataspace 4.0: Unveiling Promise of Decentralized Data-Sharing. IEEE Access. 12. 112637–112658. 6 indexed citations
6.
Timilsina, Mohan, et al.. (2024). Boosting predictive models and augmenting patient data with relevant genomic and pathway information. Computers in Biology and Medicine. 174. 108398–108398. 1 indexed citations
7.
8.
Torrente, María, Luca Costabello, Virginia Calvo, et al.. (2023). Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer. JCO Clinical Cancer Informatics. 7(7). e2200062–e2200062. 11 indexed citations
9.
Timilsina, Mohan & Alejandro Figueroa. (2023). Neural age screening on question answering communities. Engineering Applications of Artificial Intelligence. 123. 106219–106219. 3 indexed citations
10.
Timilsina, Mohan, Luca Costabello, María Torrente, et al.. (2023). Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. Expert Systems with Applications. 235. 121127–121127. 2 indexed citations
11.
Timilsina, Mohan, Dirk Fey, Luca Costabello, et al.. (2023). Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer. Journal of Biomedical Informatics. 144. 104424–104424. 5 indexed citations
12.
13.
Timilsina, Mohan, Vít Nováček, Mathieu d’Aquin, & Haixuan Yang. (2022). Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding. Neural Networks. 156. 205–217. 1 indexed citations
14.
Timilsina, Mohan, Dirk Fey, María Torrente, et al.. (2022). Integration of Clinical Information and Imputed Aneuploidy Scores to Enhance Relapse Prediction in Early Stage Lung Cancer Patients.. PubMed. 2022. 1062–1071. 2 indexed citations
15.
Tandan, Meera, et al.. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine. 131. 104249–104249. 66 indexed citations
16.
Timilsina, Mohan, Alejandro Figueroa, Mathieu d’Aquin, & Haixuan Yang. (2021). Semi-supervised regression using diffusion on graphs. Applied Soft Computing. 104. 107188–107188. 20 indexed citations
17.
Tandan, Meera, Mohan Timilsina, Martin Cormican, & Akke Vellinga. (2019). 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. 127. 127–133. 12 indexed citations
18.
Timilsina, Mohan, et al.. (2019). A Diffusion-Based Method for Entity Search. 2002. 16–23. 1 indexed citations
19.
Timilsina, Mohan, et al.. (2019). Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach. BMC Bioinformatics. 20(1). 462–462. 16 indexed citations
20.
Timilsina, Mohan, et al.. (2016). Towards predicting academic impact from mainstream news and weblogs: A heterogeneous graph based approach. ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway). 33. 1388–1389. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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