Semi-supervised learning of causal relations in biomedical scientific discourse

Improving ESP Writing Class Learning Outcomes Among Medical University Undergraduates: How Do Emotions Impact?

As English plays a significant role in most professions, improving the English for Specific Purpose (ESP) writing competence allows individuals to participate in the global professional community, which makes ESP writing important for research. However, research on ESP writing is reported to be insufficient, and how factors such as emotions affect ESP writing is rarely and marginally studied. Therefore, this study aimed at investigating how induced emotions influence the learning outcome in ESP writing classes with an emphasis on a particular rhetorical choice among medical university undergraduates. A total of 63 medical university undergraduates were recruited. After the emotional inducement, they were taught with materials selected and adopted from the BioCauses corpus and with an explicitly inductive teaching approach. Results revealed that positive emotions positively correlated with better learning outcomes, while negative emotions hindered participants in their learning. The results shed light on the impact of emotional states on ESP/medical research article (MRA) writing, learning, and teaching. Further study implications were provided accordingly.

A survey on extraction of causal relations from natural language text

As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson

BACKGROUND

Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship.

METHODS

Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers.

RESULTS

Watson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%.

CONCLUSION

Taken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.