Search bioRxiv⌕ Search

Biology subjects

Sybrandt, J.

Publications and source records attributed to Sybrandt, J..

2 recordsLinked to original sources
bioRxiv2021

Accelerating COVID-19 research with graph mining and transformer-based learning

In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone. ReproducibilityAll code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP CCS CONCEPTS* Applied computing [->] Bioinformatics; Document management and text processing; * Computing methodologies [->] Learning latent representations; Neural networks; Information extraction; Semantic networks.

bioinformatics↗
bioRxiv2018

Validation and Topic-driven Ranking for Biomedical Hypothesis Generation Systems

Literature underpins research, providing the foundation for new ideas. But as the pace of science accelerates, many researchers struggle to stay current. To expedite their searches, some scientists leverage hypothesis generation (HG) systems, which can automatically inspect published papers to uncover novel implicit connections. With no foreseeable end to the driving pace of research, we expect these systems will become crucial for productive scientists, and later form the basis of intelligent automated discovery systems. Yet, many resort to expert analysis to validate such systems. This process is slow, hard to reproduce, and takes time away from other researchers. Therefore, we present a novel method to validate HG systems, which both scales to large validation sets and does not require expert input. We also introduce a number of new metrics to automatically identify plausible generated hypotheses. Through the study of published, highly cited, and noise predicates, we devise a validation challenge, which allows us to evaluate the performance of a HG system. Using an in-progress system, MOLIERE, as a case-study, we show the utility of our validation and ranking methods. So that others may reproduce our results, we provide our code, validation data, and results at bit.ly/2EtVshN.

bioinformatics↗