Biology subjects

Wei, C.-H.

Publications and source records attributed to Wei, C.-H..

4 recordsLinked to original sources

bioRxiv2026

MorphQ: label-free quantification and visualisation of complex morphology from standardised specimen images

O_LIQuantifying complex morphology from images remains difficult because predefined descriptors capture only selected traits. Yet, supervised machine learning models for images require labels and often produce task-specific features that are hard to interpret as biological traits. C_LIO_LIWe present MorphQ, a label-free, self-supervised method that learns a quantitative morphospace from standardised specimen images. Its encoder produces feature vectors for statistical analysis, and its decoder converts analysed positions in morphospace into human-interpretable images, including hypothetical forms not represented by sampled specimens or sampled taxa. C_LIO_LIUsing 1,868 Lepidoptera species, we tested whether MorphQs label-free features were more useful for downstream analysis than features from principal component analysis (PCA) or a supervised species-classification machine learning model. As a diagnostic probe of downstream biological utility, MorphQ features supported higher low-label family-classification accuracy than comparator features, and retained stronger family-level similarity for species absent from model training, indicating better generalisation to species not seen during model training. C_LIO_LITwo case studies link MorphQ morphospaces to species-level elevation and assemblage-level functional diversity while keeping statistical patterns visually inspectable. MorphQ provides a reproducible framework for constructing interpretable morphological trait spaces when predefined descriptors are incomplete and labelled data are limited. C_LI Data/code for peer review: An anonymised repository containing the source code, trained model weights, example data, configuration files and scripts required to reproduce the analyses is available at https://anonymous.4open.science/r/MorphQ-ECD4/.

ecology↗

bioRxiv2022

A Novel Sequence Labeling Framework for Extracting Drug-Protein Relations from Biomedical Literature

Automatic extracting interactions between chemical compound/drug and gene/protein is significantly beneficial to drug discovery, drug repurposing, drug design, and biomedical knowledge graph construction. To promote the development of the relation extraction between drug and protein, the BioCreative VII challenge organized the DrugProt track. This paper describes the approach we developed for this task. In addition to the conventional text classification framework that has been widely used in relation extraction tasks, we propose a sequence labeling framework to drug-protein relation extraction. We first comprehensively compared the cutting-edge biomedical pre-trained language models for both frameworks. Then, we explored several ensemble methods to further improve the final performance. In the evaluation of the challenge, our best submission (i.e., the ensemble of models in two frameworks via major voting) achieved the F1-score of 0.795 on the official test set. Further, we realized the sequence labeling framework is more efficient and achieves better performance than the text classification framework. Finally, our ensemble of the sequence labeling models with majority voting achieves the best F1-score of 0.800 on the test set. Database URLhttps://github.com/lingluodlut/BioCreativeVII_DrugProt

bioinformatics↗

bioRxiv2019

RNAseq studies reveal distinct transcriptional response to vitamin A deficiency in small intestine versus colon, discovering novel VA-regulated genes

Vitamin A (VA) deficiency remains prevalent in resource limited countries, affecting over 250 million preschool aged children. Vitamin A deficiency is associated with reduced intestinal barrier function and increased risk of mortality due to mucosal infection. Using Citrobacter rodentium (C. rodentium) infection in mice as a model for diarrheal diseases in humans, previous reports showed reduced pathogen clearance and survival in vitamin A deficient (VAD) mice compared to their vitamin A sufficient (VAS) counterparts. ObjectivesTo characterize and compare the impact of preexisting VA deficiency on gene expression patterns in the small intestine (SI) and the colon, and to discover novel target genes in VA-related biological pathways. MethodsVAD mice were generated by feeding VAD diet to pregnant C57/BL6 dams and their post-weaning offspring. RNAseq were performed using the total mRNAs extracted from SI and colon. Differentially Expressed Gene (DEG), Gene Ontology (GO) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to characterize expression and co-expression patterns. ResultsDEGs compared between VAS and VAD groups detected 49 SI and 94 colon genes. By GO information, SI DEGs were significantly enriched in categories relevant to retinoid metabolic process, molecule binding, and immune function. Immunity related pathways, including "humoral immune response" and "complement activation" were positively associated with VA in SI. Three co-expression modules showed significant correlation with VA status in SI; these modules contained four known retinoic acid targets. In addition, other SI genes of interest (e.g. Mbl2, Cxcl14, and Nr0b2) in these modules were suggested as new candidate genes regulated by VA. Furthermore, our analysis showed that markers of two cell types in SI, mast cells and Tuft cells, were significantly altered by VA status. In colon, "cell division" was the only enriched category and was negatively associated with VA. Thus, comparison of co-expression modules between SI and colon indicated distinct networks under the regulation of dietary VA and suggest that preexisting VAD could have a significant impact on the host response to a variety of disease conditions.

physiology↗

bioRxiv2016

On expert curation and sustainability: UniProtKB/Swiss-Prot as a case study

MOTIVATIONBiological knowledgebases, such as UniProtKB/Swiss-Prot, constitute an essential component of daily scientific research by offering distilled, summarized, and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, the question of their sustainability is raised due to the growth of biomedical literature.\n\nRESULTSBy using UniProtKB/Swiss-Prot as a case study, we address this question by using different literature triage approaches. With the assistance of the PubTator text-mining tool, we tagged more than 10,000 articles to assess the ratio of papers relevant for curation. We first show that curators read and evaluate many more papers than they curate, and that measuring the number of curated publications is insufficient to provide a complete picture. We show that a large fraction of published papers found in PubMed is not relevant for curation in UniProtKB/Swiss-Prot and demonstrate that, despite appearances, expert curation is sustainable.\n\nAVAILABILITYUniProt is freely available at http://www.uniprot.org/.\n\nCONTACTsylvain.poux@sib.swiss

bioinformatics↗