Jararweh, A. | Search bioRxiv

bioRxiv2024

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

Despite their potential, current precision oncology approaches benefit only a small fraction of patients due to their limited focus on actionable genomic alterations. To expand its applicability, we propose DeepVul, a multi-task transformer-based model designed to predict gene essentiality and drug response from cancer transcriptome data. DeepVul aligns gene expressions, gene perturbations, and drug perturbations into a latent space, enabling simultaneous and accurate prediction of cancer cell vulnerabilities to numerous genes and drugs. Benchmarking against existing precision oncology approaches revealed that Deep-Vul not only matches but also complements oncogene-defined precision methods. Through interpretability analyses, DeepVul identifies underlying mechanisms of treatment response and resistance, as demonstrated with BRAF vulnerability prediction. By leveraging whole-genome transcriptome data, DeepVul enhances the clinical actionability of precision oncology, aiding in the identification of optimal treatments across a broader range of cancer patients. DeepVul is publicly available at https://github.com/alaaj27/DeepVul.git.

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bioRxiv2024

LitGene: a transformer-based model that uses contrastive learning to integrate textual information into gene representations

Representation learning approaches leverage sequence, expression, and network data, but utilize only a fraction of the rich textual knowledge accumulated in the scientific literature. We present LitGene, an interpretable transformer-based model that refines gene representations by integrating textual information. The model is enhanced through a Contrastive Learning (CL) approach that identifies semantically similar genes sharing a Gene Ontology (GO) term. LitGene demonstrates accuracy across eight benchmark predictions of protein properties and robust zero-shot learning capabilities, enabling the prediction of new potential disease risk genes in obesity, asthma, hypertension, and schizophrenia. LitGenes SHAP-based interpretability tool illuminates the basis for identified disease-gene associations. An automated statistical framework gauges literature support for AI biomedical predictions, providing validation and improving reliability. LitGenes integration of textual and genetic information mitigates data biases, enhances biomedical predictions, and promotes ethical AI practices by ensuring transparent, equitable, open, and evidence-based insights. LitGene code is open source and also available for use via a public web interface at litgene.avisahuai.com.

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