=============
Motive

9.9% of cancers are attributable to viruses according to the World Health Organization. We have identified significant areas of concern in multiple studies claiming negative association, suggesting the current understanding is not only incomplete but possibly incorrect.

We investigate under-studied questions on viruses and cancer, particularly breast, lung, and nasopharyngeal cancer.

=============
Research Areas

• AI/ML biomedical datasets: creating open-source datasets to advance the evaluation of large language models like GPT4 and Med-Gemini for biomedical purposes.

• Epstein-Barr virus (EBV) sequence conservation: addressing under-studied questions regarding EBV sequence conservation.

• EBV association with breast cancer, particularly triple-negative breast cancer (TNBC) and breast cancer: exploring the relationship between EBV and TNBC specifically and breast cancer more broadly.

• EBV association with lung cancer, particularly non-small cell lung cancer (NSCLC): exploring the relationship between EBV and NSCLC.

• EBV association with NPC, GC, and head-and-neck squamous cell carcinoma (HNSCC): exploring under-studied questions between EBV and NPC, GC, and HNSCC.

• EBV and MYC interactions: exploring the relationship between EBV and MYC.

=============
Roles

• Research associate: assist with lit review, primary analysis, paper writing, and publishing. See examples below.

• ML/AI developer: explore how ML/AI can accelerate our research. Must have basic chemistry and genomic background. Khan academy videos suffice -- degrees not needed. Must have working knowledge of LLMs and transformers.

• Software developer: must have strong regular expression and database skills.

• Bioinformatician: analyze DNA sequences and transcripts, gather genomic and transcriptomic data from various databases.