Related papers: Demonstrating Narrative Pattern Discovery from Bio…

With the proliferation of research means and computational methodologies, published biomedical literature is growing exponentially in numbers and volume. Cancer cell lines are frequently used models in biological and medical research that…

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets…

Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models…

One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end…

Researchers frequently need to synthesize their own publications into coherent narratives that demonstrate their scholarly contributions. To suit diverse communication contexts, exploring alternative ways to organize one's work while…

The Web is a potentially huge source of medical drug leads. But despite the significant amount of multi- dimensional information about drugs, currently commercial search engines accept only linear keyword strings as inputs. This work uses…

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity…

Finding interesting phenomena is the core of scientific discovery, but it is a manual, ill-defined concept. We present an integrative pipeline for automating the discovery of interesting simple hypotheses (feature-target relations with…

In this paper, we propose a novel system that integrates state-of-the-art, domain-specific large language models with advanced information retrieval techniques to deliver comprehensive and context-aware responses. Our approach facilitates…

Interactive storytelling benefits from planning and exploring multiple 'what if' scenarios. Modern LLMs are useful tools for ideation and exploration, but current chat-based user interfaces restrict users to a single linear flow. To address…

A scientific study begins with a central question, and search engines like PubMed are the first tools for retrieving knowledge and understanding the current state of the art. Large Language Models (LLMs) have been used in research,…

Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publications for informal references to…

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many…

Social media posts provide valuable insight into the narrative of users and their intentions, including providing an opportunity to automatically model whether a social media user is depressed or not. The challenge lies in faithfully…

Recent advancements in medical entity linking have been applied in the area of scientific literature and social media data. However, with the adoption of telemedicine and conversational agents such as Alexa in healthcare settings, medical…

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the Medline database. Focusing on three main branches-"diseases," "drugs and chemicals,"…

We present MARVEL (https://ligogpt.mit.edu/marvel), a locally deployable, open-source framework for domain-aware question answering and assisted scientific research. It is designed to address the increasing demands of a digital assistant…

Literature-based knowledge discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval and Natural Language Processing attempt to…

This paper presents a preliminary experimentation study using the CLEF 2017 eHealth Task 2 collection for evaluating the effectiveness of different indexing methodologies of documents and query parsing techniques. Furthermore, it is an…

The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the…