Peng Li

Peng Li

PhD student

Biomedical Informatics

The Ohio State University Comprehensive Cancer Center

About me

I am a Biomedical Informatics PhD student in the College of Medicine at The Ohio State University. My research develops and evaluates machine learning approaches for computational oncology, integrating multi-omics profiles with clinical data to study cancer progression, treatment response, and patient outcomes.

My current work spans interpretable survival modeling, graph learning, and multimodal learning in renal cell carcinoma, brain metastases, and pan-cancer settings, alongside emerging work on representation learning for large single-cell datasets.

I am very fortunate to be advised by Dr. Elshad Hasanov in the Hasanov Lab at The Ohio State University Comprehensive Cancer Center – The James.

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News

  • [2026.08] Our SNO/ASCO 2026 oral presentation on multi-omics graph neural networks was later featured by OncoDaily.
  • [2026.07] Our RCC brain metastasis survival modeling study was published in ESMO Open.
  • [2026.07] Our in-silico Phase III trial abstract was presented at AACR D3 2026.
  • [2026.06] Our ASCO 2026 RCC transcriptomic signatures work was featured by OncoDaily.
  • [2026.05] Our explainable ML/AI survival modeling study was published in Neuro-Oncology Advances.
  • [2026.05] Our renal cell carcinoma brain metastasis genomics preprint was released on bioRxiv.
  • [2026.05] Our machine learning-based transcriptomic signatures study was selected for poster presentation at the ASCO 2026 Annual Meeting.
  • [2026.05] Abstract 4526 was published in the Journal of Clinical Oncology ASCO 2026 Annual Meeting supplement.
Earlier news

Highlights

Publications

2026

  1. Li P, Majeed Z, Niazi MKK, Hasanov M, Hasanov E. Multi-omics graph neural network integration reveals predictive signatures of brain metastatic penetrance. Neuro-Oncology Advances. 2026;8(Suppl 6):vdag161.031. doi:10.1093/noajnl/vdag161.031. SNO/ASCO 2026
  2. Ozgul S, Akpinar ZF, Suki D, Li P, Majeed Z, Acikgoz Y, Ferguson SD, Jonasch E, Hasanov M, Hasanov E. Era-specific prognostic factors and an interpretable machine-learning survival model for renal cell carcinoma brain metastases. Neuro-Oncology Advances. 2026;8(Suppl 6):vdag161.027. doi:10.1093/noajnl/vdag161.027. SNO/ASCO 2026
  3. Ali MIH, Majeed Z, Li P, Verschraegen CF, Niazi K, Hasanov E, Hasanov M. An optimized machine learning model for overall survival prediction in brain metastasis patients using genomic mutation and copy number features. Neuro-Oncology Advances. 2026;8(Suppl 6):vdag161.028. doi:10.1093/noajnl/vdag161.028. SNO/ASCO 2026
  4. Ozgul S, Akpinar ZF, Suki D, Li P, Majeed Z, Acikgoz Y, Ferguson SD, Jonasch E, Hasanov M, Hasanov E. Era-specific prognostic factors and an interpretable machine-learning survival model for renal cell carcinoma brain metastases. ESMO Open. 2026;11:108300. doi:10.1016/j.esmoop.2026.108300. IF = 10.6
  5. Majeed Z, Li P, Verschraegen CF, Hays JL, Niazi MKK, Hasanov M, Hasanov E. In-silico Phase III Clinical Trial of Avelumab Plus Axitinib Versus Sunitinib in Advanced Renal Cell Carcinoma Using Machine Learning Model Transfer Approach. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clinical Cancer Research. 2026;32(14 Suppl):A026. doi:10.1158/1557-3265.D32026-A026. AACR D3 2026
  6. Majeed Z, Ozgul S, Li P, Suki D, Acikgoz Y, Jonasch E, Ferguson SD, Hasanov M, Hasanov E. Explainable ML/AI Model for Estimating Postoperative Survival in Renal Cell Carcinoma Brain Metastasis Patients. Neuro-Oncology Advances. 2026;vdag140. doi:10.1093/noajnl/vdag140. IF = 4.6
  7. Gok Yavuz B, Li P, Ovando-Ricardez JA, La Ferlita A, Tse JW, Hanalioglu S, Babaoglu B, Baylarov B, Norberg LM, Chancoco H, Thompson E, Mut M, Soylemezoglu F, Huse JT, Osunkoya AO, Bilen MA, Hasanov M, Jonasch E, Shih DJ, Hasanov E. Deciphering the genomic landscape of renal cell carcinoma brain metastases. bioRxiv. 2026:2026.05.02.722447. doi:10.64898/2026.05.02.722447. bioRxiv
  8. Li P, Majeed Z, Ozgul S, Ali MIH, Single N, Stover DG, Makary MS, Bicer F, Mortazavi A, Rathmell WK, Singer EA, Niazi MKK, Wu R, Hasanov M, Hasanov E. Machine learning-based transcriptomic signatures to predict treatment outcomes across targeted and immunotherapy regimens in renal cell carcinoma. Journal of Clinical Oncology. 2026;44(suppl 16):4526. doi:10.1200/JCO.2026.44.16_suppl.4526. ASCO 2026
  9. Gok Yavuz B, Li P, Ovando-Ricardez JA, La Ferlita A, Tse JW, Hanalioglu S, Babaoglu B, Baylarov B, Norberg LM, Chancoco H, Thompson E, Mut M, Soylemezoglu F, Huse JT, Osunkoya AO, Bilen MA, Hasanov M, Jonasch E, Shih DJ, Hasanov E. Deciphering the genomic landscape of renal cell carcinoma brain metastases. Journal of Clinical Oncology. 2026;44(suppl 16):4524. doi:10.1200/JCO.2026.44.16_suppl.4524. ASCO 2026
  10. Ali MI, Majeed Z, Li P, Verschraegen CF, Niazi K, Hasanov E, Hasanov M. An optimized machine learning model for overall survival prediction in brain metastasis patients using genomic mutation and copy number features. Journal of Clinical Oncology. 2026;44(suppl 16):e14002. doi:10.1200/JCO.2026.44.16_suppl.e14002. ASCO 2026
  11. Majeed Z, Li P, Verschraegen CF, Hays JL, Niazi K, Hasanov M, Hasanov E. In silico phase III clinical trial of avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma using a machine learning model transfer approach. Journal of Clinical Oncology. 2026;44(suppl 16):e16500. doi:10.1200/JCO.2026.44.16_suppl.e16500. ASCO 2026
  12. Abu Alragheb B, Ozgul S, Ali MI, Acikgoz Y, Abu Alragheb R, Li P, Majeed Z, Cevik L, Niazi K, Wu RC, Hasanov M, Hasanov E. Decoding cancers of unknown primary through genomics-driven clustering: a pan-cancer framework for prognostic classification using AACR GENIE data. Journal of Clinical Oncology. 2026;44(suppl 16):e15006. doi:10.1200/JCO.2026.44.16_suppl.e15006. ASCO 2026

Academic Service

Peer reviewer for Journal of King Saud University - Computer and Information Sciences, Data Mining and Knowledge Discovery, Scientific Reports, BMC Bioinformatics, and International Journal of Machine Learning and Cybernetics.