Presenter: Susan Gregurick, PhD

This talk will focus on current activities and achievements from the Office of Data Science Strategy and highlight the strategic vision of data science at NIH, with emphasis in the areas of artificial intelligence, health informatics, data resources, and computational biosciences.


Presenter: Yana Najjar, MD

The treatment paradigm of patients with advanced melanoma has changed drastically with the advent of anti-PD1 based immunotherapy. However, in spite of unprecedented response rates, the fact remains that many patients do not respond to treatment. We will discuss the role of tumor cell metabolism in advanced melanoma, its impact on the tumor microenvironment, and how the tumor microenvironment may be remodled so that more patients can benefit from immunotherapy.

Presenter: Steven Handler, MD, PhD

Potentially avoidable hospitalizations of nursing home residents are common and result in increased morbidity, mortality, and excess healthcare expenditures.

Presenter: Faina Linkov, PhD, MPH

Research dollars are increasingly difficult to get, however quality research can be done inexpensively by harnessing existing informational repositories. Research focusing on existing data is especially important in healthcare, where value of care (increasing quality of care at a lower cost) is becoming an important goal for patients, providers, and health plans.

Presenter: Lifan Liang, MS

The matrix factorization is an important way to analyze co-regulation patterns in transcriptomic data, which can reveal the tumor signal perturbation status and subtype classification. However, current matrix factorization methods do not provide clear bicluster structure.

Presenter: George Tseng, PhD

Model organisms have been widely used in biomedical research to replace human studies and to reduce cost and experimental time in initial biomedical exploration. Recent contradictory reports on whether mouse models mimic human in transcriptomic response (PNAS, 2013 3507-3512; PNAS, 2015 1167-1172) have created debates on usefulness of animal models.

Presenter: Takis Benos, PhD

The Benos’ group develops computational, machine learning methods to address important questions in medicine.   We are interested in identifying the factors that affect chronic disease onset and progression and cancer survival.  We also develop predictive methods and tools that can directly improve health.  To do so, we use probabilistic graphical models and other machine learning methods that can integrate and mine high-dimensional, multi-modal data.

Presenter: Shou-Jiang Gao, PhD

Dr. Shou-Jiang Gao’s laboratory is interested in cancer viruses and their associated cancers with the current focus on Kaposi’s sarcoma-associated herpesvirus (KSHV) and AIDS-related malignancies. Dr. Gao’s lab uses a systems approach to dissect KSHV-cell interactions and delineate the mechanisms of KSHV infection and cellular transformation.