Biology subjects

Wolstencroft, K.

Publications and source records attributed to Wolstencroft, K..

3 recordsLinked to original sources

bioRxiv2025

LncRNA-BERT: An RNA Language Model for classifying Coding and Long Non-Coding RNA

Understanding (novel) RNA transcripts generated in next generation sequencing experiments requires accurate classification, given the increasing evidence that long non-coding RNAs (lncRNAs) play crucial regulatory roles. Recent developments in Large Language Models present opportunities for classifying RNA coding potential with sequence-based algorithms that can overcome the limitations of classical approaches that assess coding potential based on a set of predefined features. We present lncRNA-BERT, an RNA language model pre-trained and fine-tuned on human RNAs collected from the GENCODE, RefSeq, and NONCODE databases to classify lncRNAs. LncRNA-BERT matches and outperforms state-of-the-art classifiers on three test datasets, including the cross-species RNAChallenge benchmark. The pre-trained lncRNA-BERT model distinguishes coding from long non-coding RNA without supervised learning which confirms that coding potential is a sequenceintrinsic characteristic. LncRNA-BERT has been shown to benefit from pre-training on human data from GENCODE, RefSeq, and NONCODE, improving upon configurations pre-trained on the commonly used RNAcentral dataset. In addition, we propose a novel Convolutional Sequence Encoding method that is shown to be more effective and efficient than K-mer Tokenization and Byte Pair Encoding for training with long RNA sequences that are otherwise above the common context window size. lncRNA-BERT is available at https://github.com/luukromeijn/lncRNA-Py.

bioinformatics↗

bioRxiv2024

Knowledge Graphs and Explainable AI for Drug Repurposing on Rare Diseases

Artificial Intelligence (AI)-based drug repurposing is an emerging strategy to identify drug candidates to treat rare diseases. However, cutting-edge algorithms based on Deep Learning (DL) typically dont provide a human understandable explanation supporting their predictions. This is a problem because it hampers the biologists ability to decide which predictions are the most plausible drug candidates to test in costly lab experiments. In this study, we propose rd-explainer a novel AI drug repurposing method for rare diseases which obtains possible drug candidates together with human understandable explanations. The method is based on Graph Neural Network (GNN) technology and explanations were generated as semantic graphs using state-of-the-art eXplainable AI (XAI). The model learns features from current background knowledge on the target rare disease structured as a Knowledge Graph (KG), which integrates curated facts and their evidence on different biomedical entities such as symptoms, drugs, genes and ortholog genes. Our experiments demonstrate that our method has excellent performance that is superior to state-of-the-art models. We investigated the application of XAI on drug repurposing for rare diseases and we prove our method is capable of discovering plausible drug candidates based on testable explanations. The data and code are publicly available at https://github.com/PPerdomoQ/rare-disease-explainer. HighlightsO_LIWe demonstrated the use of graph-based explainable AI for drug repurposing on rare diseases to accelerate sound discovery of new therapies for this underrepresented group. C_LIO_LIWe developed rd-explainer for rare disease specific drug research for faster translation. It predicts drugs to treat symptoms/phenotypes, it is highly performant and novel candidates are plausible according to evidence in the scientific literature and clinical trials. Key is that it learns a GNN model that is trained on a knowledge graph built specifically for a rare disease. We provide rd-explainer code freely available for the community. C_LIO_LIrd-explainer is researcher-centric interpretable ML for hypothesis generation and lab-in-the-loop drug research. Explanations of predictions are semantic graphs in line with human reasoning. C_LIO_LIWe detected an effect of knowledge graph topology on explainability. This highlights the importance of knowledge representation for the drug repurposing task. C_LI

bioinformatics↗

bioRxiv2017

Identifiers for the 21st century:How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

In many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

bioinformatics↗