Biology subjects

Allauzen, A.

Publications and source records attributed to Allauzen, A..

3 recordsLinked to original sources

bioRxiv2026

DORA: a dose-response autoencoder for interpretable transcriptome-to-viability prediction

Predicting the effect of drugs on cell viability is a central challenge in drug discovery. Artificial intelligence holds the promise to considerably accelerate this process by leveraging rich cellular data such as transcriptomics. Current models focus on either transcriptomes or inhibitory concentrations, but they fall short in integrating these sources of information. Here, we propose DORA (Dose-Response Autoencoder), a deep learning model that predicts changes in transcriptomes and viability in a dose-dependent manner, knowing the unperturbed cell state. By enforcing a latent space consistent with cumulative dose effects, DORA matches other methods at predicting transcriptomes and substantially outperforms existing latent representations at viability prediction. The transcriptome-viability relationship provided by the model further allows the recovery of known biomarkers of cell viability while suggesting novel ones. Overall, DORA provides a unified framework delivering actionable biological insights for phenotypic drug screening and personalized medicine.

molecular biology↗

bioRxiv2024

A Guide for Active Learning in Synergistic Drug Discovery

Synergistic drug combination screening is a promising strategy in drug discovery, but it involves navigating a costly and complex search space. While AI, particularly deep learning, has advanced synergy predictions, its effectiveness is limited by the low occurrence of synergistic drug pairs. Active learning, which integrates experimental testing into the learning process, has been proposed to address this challenge. In this work, we explore the key components of active learning to provide recommendations for its implementation. We find that molecular encoding has a limited impact on performance, while the cellular environment features significantly enhance predictions. Additionally, active learning can discover 60% of synergistic drug pairs with only exploring 10% of combinatorial space. The synergy yield ratio is observed to be even higher with smaller batch sizes, where dynamic tuning of the exploration-exploitation strategy can further enhance performance.

bioinformatics↗

bioRxiv2024

Hypothesis-driven interpretable neural network for interactions between genes

Mechanistic models of genetic interactions are rarely feasible due to a lack of information and computational challenges. Alternatively, machine learning (ML) approaches may predict gene interactions if provided with enough data but they lack interpretability. Here, we propose an ML approach for interpretable genotype-to-fitness mapping, the Direct-Latent Interpretable Model (D-LIM). The neural network is built on a strong hypothesis: mutations in different genes cause independent effects in phenotypes, which then interact via non-linear relationships to determine fitness. D-LIM predicts interpretable genotype-to-fitness maps with state-of-the-art accuracy for gene-to-gene and gene-to-environment perturbations in deep mutational scanning of a metabolic pathway, a protein-protein interaction system, and yeast mutants for environmental adaptation. The hypothesis-driven structure of D-LIM offers interpretable features reminiscent of mechanistic models: the inference of phenotypes, identification of trade-offs, and fitness extrapolation outside of the data domain.

bioinformatics↗