BfBio: a graph-based tool for the prediction of Angiogenic Stalk Cell genes using a Personalized PageRank algorithm
- 1VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
Abstract
Although most human protein coding genes have functional annotations in databases, such as GeneCards, many remain poorly characterized. To address this gap, computational tools can be leveraged to predict the functional roles of under-annotated genes by extracting patterns from complex biological networks. Here we introduce Brain-for-Biotech (BfBio), a framework designed to identify genes important for vascular endothelial cells (EC), which are crucial cells for vessel formation (angiogenesis), vascular homeostasis, hemostasis and blood/tissue barrier function but also critical mediators of immunity and cancer progression. BfBio utilizes a Personalized PageRank (PPR) algorithm on an integrated network of different omics datasets and publicly available gene-gene/protein-protein interaction databases. In this study, we apply the predictive capabilities of BfBio to infer angiogenic stalk cell phenotype function in genes for which this function was not known before. By leveraging a set of genes characterizing the stalk cell cluster in lung tumor EC models previously identified, we have achieved a high Area Under Receiver Operative Characteristic (AUC-ROC) performance (0.837). Enrichment analysis, coupled with a text mining application, further confirmed that among the 49 predicted genes four of them were poorly characterized yet possessed biologically relevant properties and were linked to cancer, thereby validating BfBio as a robust tool for prioritizing novel therapeutic targets in vascular biology.
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BibTeX
@misc{bettoni2026bfbio,
title = {{BfBio: a graph-based tool for the prediction of Angiogenic Stalk Cell genes using a Personalized PageRank algorithm}},
author = {L. Bettoni and J. Dmitrieva and M. Mousa and H. Alsafar and Y. Saeys and P. Zakeri and N. Veiga and P. Carmeliet},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.23.746493},
url = {https://www.biorxiv.org/content/10.64898/2026.08.23.746493v3},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - BfBio: a graph-based tool for the prediction of Angiogenic Stalk Cell genes using a Personalized PageRank algorithm
AU - L. Bettoni
AU - J. Dmitrieva
AU - M. Mousa
AU - H. Alsafar
AU - Y. Saeys
AU - P. Zakeri
AU - N. Veiga
AU - P. Carmeliet
PY - 2026
DA - 2026/08/23
PB - bioRxiv
DO - 10.64898/2026.08.23.746493
UR - https://www.biorxiv.org/content/10.64898/2026.08.23.746493v3
AB - Although most human protein coding genes have functional annotations in databases, such as GeneCards, many remain poorly characterized. To address this gap, computational tools can be leveraged to predict the functional roles of under-annotated genes by extracting patterns from complex biological networks. Here we introduce Brain-for-Biotech (BfBio), a framework designed to identify genes important for vascular endothelial cells (EC), which are crucial cells for vessel formation (angiogenesis), vascular homeostasis, hemostasis and blood/tissue barrier function but also critical mediators of immunity and cancer progression. BfBio utilizes a Personalized PageRank (PPR) algorithm on an integrated network of different omics datasets and publicly available gene-gene/protein-protein interaction databases. In this study, we apply the predictive capabilities of BfBio to infer angiogenic stalk cell phenotype function in genes for which this function was not known before. By leveraging a set of genes characterizing the stalk cell cluster in lung tumor EC models previously identified, we have achieved a high Area Under Receiver Operative Characteristic (AUC-ROC) performance (0.837). Enrichment analysis, coupled with a text mining application, further confirmed that among the 49 predicted genes four of them were poorly characterized yet possessed biologically relevant properties and were linked to cancer, thereby validating BfBio as a robust tool for prioritizing novel therapeutic targets in vascular biology.
ER -