SEA January: Information Retrieval in the Medical Domain

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IMPORTANT: You will be able to view the Zoom link once you 'attend' the meetup on this page.
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SEA is back again in 2022! The first edition of the year will all be about information retrieval in the medical domain. With Anna Gogleva from AstraZeneca and Dustin Wright from the University of Copenhagen.

17.00: Dustin Wright (University of Copenhagen)

Title: Semi-Supervised Exaggeration Detection of Health Science Press Releases

Abstract: Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. In this talk, I will discuss our recent work on the automatic detection of exaggeration in scientific press releases, a first step in building tools to improve the communication of science. To do this, we curate a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task.

17.30: Anna Gogleva (AstraZeneca)

Title: Drug Discovery as a Recommendation Problem: Challenges and Complexities in Biological Decisions

Abstract: Drug discovery is notorious for its low success rates. Despite best research efforts, the majority of drugs fail at early stages of development, even before they enter clinical trials. This phenomenon stems from the inherent complexity of biological systems and our poor understanding of human diseases. To improve that understanding, swaths of data have been generated in recent years. Still, data does not easily translate into knowledge or actionable insights. Here we explore how approaches from the recommendation system domain could help scientists comprehend the ever-growing amount of biomedical facts. In this talk we will further focus on building a recommendation system on top of a heterogeneous knowledge graph to find key genes driving drug resistance in lung cancer.

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This is (still) an online event. The URL will be shared close to the day of the event. All times are Amsterdam times.

Into numbers? These SEA talks are SEA talks #187 and #188.