The exponential growth of biomedical literature has made manual curation of biological interaction networks increasingly difficult. Existing automated biological interaction extraction systems address the scaling challenge but treat extraction as a final step, delivering structured output with limited or no integrated support for biologists to interactively verify, correct and contextually interrogate extracted interactions against their source evidence within the same environment. We present Knowledge-Assisted Literature Mining for Biological Interaction Analysis (KALIMBA), an end-to-end, human-in-the-loop platform that integrates three complementary extraction methods (NLP-only, LLM-only, and hybrid) alongside expert annotation and evidence-grounded conversational querying through retrieval-augmented generation (RAG) chat module driven by a dual-context prompt, within a single unified workflow. Evaluation on a corpus of 40 signaling-focused papers demonstrates that the LLM-only back-end recovers substantially more interactions than the NLP-only approach. RAG chat evaluation by a domain expert confirms that the conversational module provides scientifically grounded responses that support curation decisions beyond what the structured interaction table alone conveys.

We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, applying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.

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.

In a recent project, the Language Application Grid was augmented to support the mining of scientific publications. The results of that ef- fort have now been repurposed to focus on Covid-19 literature, including modification of the LAPPS Grid “AskMe” query and retrieval engine. We describe the AskMe system and discuss its functionality as compared to other query engines available to search covid-related publications.