The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target–disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene–disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.

Abstract.Enthymeme reconstruction, i.e. the task of reformulating arguments with missing propositions, is an exciting task at the borderline of text understanding and argument interpretation. However, there is some doubt in the community about the feasibility of this task due to the wide range of possible reformulations that are open to humans. We therefore believe that research on how to define an objective ground truth for these tasks is necessary before any work on the automatic reconstruction can begin.Here, we present a feasibility study for the task of finding and expanding enthymemes involving a fortiori arguments in realworld texts, and we show that given a sufficiently strict reformulation of the human annotation task, substantial agreement can be achieved. We split the task into three sub-tasks: 1. deciding whether a candidate text span really represents an enthymematic argument, 2. classifying the type of a fortiori argument concerned and 3. describing the missing premise in natural language. In a case study involving the two authors of this paper as annotators, we test a specific type of a fortiori arguments, the let alone construction, for its suitability for reaching high agreement in all three stages of the task. We also discuss pragmatic effects of let alone and how they relate to argumentation theory.

This paper describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the highest mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPET LARGE and an ensemble of XLM-RoBERTa LARGE with adapters and task adaptive pretraining. For Subtask 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the remaining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques. 1