Context-aware query design combines knowledge and data for efficient reading and reasoning

Abstract

The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.

Context-driven interaction retrieval and classification for modeling, curation, and reuse

“…However, these interactions often lack context information such as cell type or intracellular location of interaction, making it challenging to identify the most relevant information. Carefully selecting query terms to search for and identify most relevant papers can help limit the machine reading and INDRA output to the desired context 10 . The machine reading output can also be filtered by FLUTE 11 , which relies on existing knowledge bases to output only high-confidence interactions confirmed by expert opinion or experiments.…”