Biology subjects

Corbeil, J.

Publications and source records attributed to Corbeil, J..

7 recordsLinked to original sources

bioRxiv2026

QMAP: A Benchmark for Standardized Evaluation of Antimicrobial Peptide MIC and Hemolytic Activity Regression

Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics, but progress in computational AMP discovery has been difficult to quantify due to inconsistent datasets and evaluation protocols. We introduce QMAP, a domain-specific benchmark for predicting AMP antimicrobial potency (MIC) and hemolytic toxicity (HC50) with homology-aware, predefined test sets. QMAP enforces strict sequence homology constraints between training and test data, ensuring that model performance reflects true generalization rather than overfitting. Applying QMAP, we reassess existing MIC models and establish baselines for MIC and HC50 regression. Results suggest limited progress over six years, poor performance for high-potency MIC regression, and low predictability for hemolytic activity, emphasizing the need for standardized evaluation and improved modeling approaches for highly potent peptides. We release a Python package facilitating practical adoption, and with a Rust-accelerated engine enabling efficient data manipulation, installable with pip install qmap-benchmark.

bioinformatics↗

bioRxiv2024

Turncoat antibodies unmasked in a model of autoimmune demyelination: from biology to therapy

Autoantibodies contribute to many autoimmune diseases, yet there is no approved therapy to neutralize them selectively. A popular mouse model, experimental autoimmune encephalomyelitis (EAE), could serve to develop such a therapy, provided we can better understand the nature and importance of the autoantibodies involved. Here we report the discovery of autoantibody-secreting extrafollicular plasmablasts in EAE induced with specific myelin oligodendrocyte glycoprotein (MOG) antigens. Single-cell RNA sequencing reveals that these cells produce non-affinity-matured IgG antibodies. These include pathogenic antibodies competing for shared binding space on MOGs extracellular domain. Interestingly, the synthetic anti-MOG antibody 8-18C5 can prevent the binding of pathogenic antibodies from either EAE mice or people with MOG antibody disease (MOGAD). Moreover, an 8-18C5 variant carrying the NNAS mutation, which inactivates its effector functions, can reduce EAE severity and promote functional recovery. In brief, this study provides not only a comprehensive characterization of the humoral response in EAE models, but also a proof of concept for a novel therapy to antagonize pathogenic anti-MOG antibodies.

immunology↗

bioRxiv2022

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictions

MotivationBreakthroughs in high-throughput technologies and machine learning methods have enabled the shift towards multi-omics modelling as the preferred means to understand the mechanisms underlying biological processes. Machine learning enables and improves complex disease prognosis in clinical settings. However, most multi-omic studies primarily use transcriptomics and epigenomics due to their over-representation in databases and their early technical maturity compared to others omics. For complex phenotypes and mechanisms, not leveraging all the omics despite their varying degree of availability can lead to a failure to understand the underlying biological mechanisms and leads to less robust classifications and predictions. ResultsWe proposed MOT (Multi-Omic Transformer), a deep learning based model using the transformer architecture, that discriminates complex phenotypes (herein cancer types) based on five omics data types: transcriptomics (mRNA and miRNA), epigenomics (DNA methylation), copy number variations (CNVs), and proteomics. This model achieves an F1-score of 98.37% among 33 tumour types on a test set without missing omics views and an F1-score of 96.74% on a test set with missing omics views. It also identifies the required omic type for the best prediction for each phenotype and therefore could guide clinical decisionmaking when acquiring data to confirm a diagnostic. The newly introduced model can integrate and analyze five or more omics data types even with missing omics views and can also identify the essential omics data for the tumour multiclass classification tasks. It confirms the importance of each omic view. Combined, omics views allow a better differentiation rate between most cancer diseases. Our study emphasized the importance of multi-omic data to obtain a better multiclass cancer classification. Availability and implementationMOT source code is available at https://github.com/dizam92/multiomic_predictions.

bioinformatics↗

bioRxiv2020

Fast protein database as a service with kAAmer

Identification of proteins is one of the most computationally intensive steps in genomics studies. It usually relies on aligners that dont accommodate rich information on proteins and require additional pipelining steps for protein identification. We introduce kAAmer, a protein database engine based on amino-acid k-mers, that supports fast identification of proteins with complementary annotations. Moreover, the databases can be hosted and queried remotely.

bioinformatics↗

bioRxiv2018

Interpretable genotype-to-phenotype classifiers with performance guarantees

Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validated through an application to the genomic prediction of antimicrobial resistance, an important public health concern. Highly accurate models were obtained for 12 species and 56 antibiotics, and their interpretation revealed known resistance mechanisms, as well as some potentially new ones. An open-source disk-based implementation that is both memory and computationally efficient is provided with this work. The implementation is turnkey, requires no prior knowledge of machine learning, and is complemented by comprehensive tutorials.

genomics↗

bioRxiv2018

Mass Spectra alignment using virtual lock-masses

Mass spectrometry is a valued method to evaluate the metabolomics content of a biological sample. The recent advent of rapid ionization technologies such as Laser Diode Thermal Desorption (LDTD) and Direct Analysis in Real Time (DART) has rendered high-throughput mass spectrometry possible. It is used for large-scale comparative analysis of populations of samples. In practice, many factors resulting from the environment, the protocol, and even the instrument itself, can lead to minor discrepancies between spectra, rendering automated comparative analysis difficult. In this work, a sequence/pipeline of algorithms to correct variations between spectra is proposed. The algorithms correct multiple spectra by identifying peaks that are common to all and, from those, computes a spectrum-specific correction. We show that these algorithms increase comparability within large datasets of spectra, facilitating comparative analysis, such as machine learning.

bioinformatics↗

bioRxiv2016

Genome-wide screen for haploinsufficient cell size genes in the opportunistic yeast Candida albicans

One of the most critical but still poorly understood aspects of eukaryotic cell proliferation is the basis for commitment to cell division in late G1 phase called Start in yeast and the Restriction Point in metazoans. In all species, a critical cell size threshold coordinates cell growth with cell division and thereby establishes a homeostatic cell size. While a comprehensive survey of cell size genetic determinism has been performed in the saprophytic yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe, very little is known in pathogenic fungi. As a number of critical Start regulators are haploinsufficient for cell size, we applied a quantitative analysis of the size phenome, using elutriation-barcode sequencing methodology, to 5,639 barcoded heterozygous deletion strains in the opportunistic yeast Candida albicans. Our screen identified conserved known regulators and biological processes required to maintain size homeostasis in addition to novel C. albicans specific size genes, and provided a conceptual framework for future mechanistic studies. Interestingly, some of the size genes identified were required for fungal pathogenicity suggesting that cell size homeostasis may be elemental to C. albicans fitness or virulence inside the host.

genetics↗