Biology subjects

Faux, N.

Publications and source records attributed to Faux, N..

3 recordsLinked to original sources

bioRxiv2023

Finding and exploring reproducible cell phenotypes with the Stemformatics data portal

Stemformatics is an established online data portal which hosts hundreds of curated gene expression datasets. It has been serving the stem cell research community for over a decade, by hosting transcriptional profiles of pluripotent and adult stem cells and their progeny from multiple tissues and derivation methods. The portal provides easy-to-use online tools to explore gene expression patterns in published data. In recent years, Stemformatics has shifted its focus from curation to collation and integration of public data with shared phenotypes. It now hosts several integrated expression atlases based on human myeloid cells, which allow for easy cross-dataset comparisons and discovery of emerging cell subsets and activation properties. The inclusion of laboratory-derived cell types enables users to benchmark their own data, to assist with cell-type standardisation or improve cell-derivation methods. The sample annotations have been greatly improved to enable better data integration, and the website has also undergone a major upgrade to modernise its visualisation tools. An application programming interface server also provides the data directly for computational users. Stemformatics is an open-source project and readily available at stemformatics.org.

systems biology↗

bioRxiv2022

BioAct: Biomedical Knowledge Base Construction using Active Learning

Creating and curating knowledge resources has been a paramount activity in the biomedical domain. In recent years, automated methods for knowledge base construction have flourished and have enabled large scale construction and curation of such resources. In the biological domain, techniques such as next generation sequencing produce new data at exponential rate, making mere manual curation of knowledge resources simply unfeasible. The major technology to automate knowledge base construction is Information Extraction -- specifically tasks such as Named Entity Recognition or Relation Extraction. The major hurdle for IE methods is the availability of labelled data for training, which can be prohibitively expensive and challenging to obtain due to the need of domain experts. Active learning aims at minimizing the cost of manual labelling by only requiring it for smaller and more useful portions of the data. With this motivation, we devised a method to quickly construct highly curated datasets to enable biomedical knowledge base construction. The method, named BioAct, is based on a partnership between automatic annotation methods (leveraging SciBERT with other machine learning models) and subject matter experts and uses active learning to create training datasets in the biological domain. The main contribution of this work is twofold; in addition to the BioAct method itself, we publicly release an annotated dataset on antimicrobial resistance, produced by a team of subject matter experts using BioAct. Additionally, we simulate a knowledge base construction task using the MegaRes and CARD knowledge bases to provide insight and lessons learned about the usefulness of the annotated dataset for this task.

bioinformatics↗

bioRxiv2018

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature

As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).

bioinformatics↗