MOTIVATION: Automatic information extraction from biomedical texts requires machine learning methodology that can recognize biomedical entities, characterize inter-entity relationships, and relate extracted information to specific research topics. Large language models (LLMs) excel in general tasks but perform less reliably in the biomedical domain, where texts are characterized by extensive technical terminology and semantic variations from general literature. There is an unmet need for annotated full-text datasets that can be used to fine-tune language models for significant biomedical applications. Here, we focus on extraction of the complex relationships between genes and diseases. RESULTS: We present BioTriplex, a corpus of 100 full-length biomedical research articles (comprising 604 subsection texts) manually annotated with disease names, genes, and 21 subtypes of disease-gene relationships. We employ BioTriplex to train the LLaMA 3.1 8B language model in gene-disease relation extraction. Our fine-tuned model outperforms zero-shot and few-shot approaches, both within the LLaMA 3.1 architecture and across the larger state-of-the-art LLMs GPT-4 and Claude Sonnet 3.7, and classifies gene-disease relation types with broader scope and greater granularity than previously described. These results validate BioTriplex as a useful full-text data resource and underscore the value of specialized datasets in fine-tuning language models for important biomedical tasks. AVAILABILITY AND IMPLEMENTATION: https://github.com/PanagiotisFytas/BioTriplex.

MOTIVATION: Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies. RESULTS: We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts. AVAILABILITY AND IMPLEMENTATION: Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.

Eye tracking technologies have frequently been used in sport research to understand the interrelations between gaze behavior and performance, using a paradigm known as vision-for-action. This methodology has not been robustly applied within the field of interface design. The present work demonstrates the benefit of employing a vision-for-action paradigm for interface evaluation. This is demonstrated through the evaluation of a novel task-specific symbology set presented on a head-up-display (HUD), developed to support pilots conduct ground operations in low-visibility conditions. HUD gaze behavior was correlated with task performance to determine whether certain combinations of gaze behavior could produce effective predictive performance models. A human-in-the-loop experiment was conducted with 11 professional pilots who were required to taxi in a fixed-base flight simulator using the HUD symbology, while gaze data toward the different HUD symbology elements was collected. Performance was measured as centerline deviation error and taxiing speed. Results revealed that appropriately timed gaze behavior toward task-specific elements of the HUD were associated with superior performance. During turns, attention toward an undercarriage lateral position indicator was associated with reduced centerline deviation (p< 0.05). The findings are interpreted alongside detailed posttrial user-feedback of the HUD symbology to illustrate how eye tracking methodologies can be incorporated into interface usability evaluations. The joint interpretation of these data demonstrates these novel procedures, the findings contribute to enhancing the wider domain of interface design evaluation.

Zaiqiao Meng, Fangyu Liu, Ehsan Shareghi, Yixuan Su, Charlotte Collins, Nigel Collier. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.