Research

DMIS Lab focuses on Biomedical AI, NLP, Graph ML and other topics. Below are some exemplary works produced by lab members. More papers related to each of these topics can be found in the publications section.

Biomedical AI

Keywords: Drug Discovery, Personalized Medicine, Systems Biology

Biomedical data and the field of bioinformatics holds much potential to benefit human life. Computers have been used in order to take advantage of this data gold mine. Despite advances in computer and information technology, tasks that use biomedical data, like drug prescription and discovery, are complicated and influenced by many factors not accountable by human beings. DMIS Lab aims to address this issue by developing full-scale machine learning algorithms that may further advance existing biomedical data mining and bioinformatics methods and techniques in order to facilitate this process. Such methods provide much practical value in real-world applications.

DMIS Lab leverages large-scale foundation models and multi-omics data integration to accelerate AI-driven drug discovery and target identification.
Research focuses on precision medicine approaches, including patient stratification and personalized treatment response prediction using deep learning.

Natural Language Processing (NLP)

Keywords: Biomedical NLP, Question Answering, Named Entity Recognition

Natural language processing (NLP) is the field of studying how computers may understand and utilize human language. DMIS Lab conducts research in various subfields of NLP including question answering (QA), named entity recognition (NER), large language models (LLMs), etc. This research effort also spills over to biomedical AI due to the fact that many bioinformatics tasks rely on information from various biomedical corpora. DMIS Lab is currently developing such tools including biomedical NER, biomedical QA, etc. Below are some examples from our recent work:

DMIS Lab actively develops and applies large language models (LLMs) for biomedical and clinical NLP tasks, including medical question answering and clinical information extraction.
Ongoing research explores retrieval-augmented generation (RAG) and instruction-tuned biomedical LLMs to improve reliability and factual accuracy in healthcare applications.

Graph ML & Machine Learning Applications (Graph & ML)

Keywords: Graph Representation Learning, Computer Vision, Recommender Systems

Machine learning methods that are based on graph structures have gained much popularity as they provide utility for data in the form of graphs. As objects like drugs or chemicals may be represented by graphs of various forms, DMIS Lab focuses on develop machine learning algorithms that utilize such graphical structures. In addition to graph-based models, our lab also conducts research on more fundamental aspects of the field of ML in order to fine tune the effectiveness of our current tools and methods and enhance our understanding of the algorithms we use.

DMIS Lab develops graph neural network (GNN) models for biomedical knowledge graphs, enabling tasks such as drug-target interaction prediction and disease network analysis.