Biology subjects

Deangeli, G.

Publications and source records attributed to Deangeli, G..

3 recordsLinked to original sources

bioRxiv2025

Proteomizer: Leveraging the Transcriptome-Proteome Mismatch to Infer Novel Gene Regulatory Relations

The correlation between transcriptomic (Tx) and proteomic (Px) profiles remains modest, typically around r = 0.5 across genes and r = 0.3 across samples, limiting the utility of transcriptomic data as a proxy for protein abundance. To address this, we introduce Proteomizer, a deep learning platform designed to infer a samples Px landscape from its Tx and miRNomic (Mx) profiles. Trained on 8,613 matched Tx-Mx-Px samples from TCGA and CPTAC, Proteomizer achieved a Tx-Px correlation of r = 0.68, representing the highest performance reported to date for this task. We further developed a Monte Carlo simulation framework to evaluate the impact of proteomization on differential expression analysis. Proteomizer substantially improved the accuracy of differential gene expression detection, with p-value precision increasing by up to 62-fold, and by as much as six orders of magnitude for a subset of genes enriched in mitochondrial and ribosomal functions. However, performance gains did not generalize to unseen tissue types or datasets generated using different protocols. Finally, we applied explainable AI (XAI) techniques to identify regulatory relations contributing to Tx-Px discrepancies. Our predictions from 100 highly annotated genes were cross-compared against by a literature-based biological knowledge graph of 322 million annotations: our explainers achieved a ROC-AUC of 0.74 in predicting miRNA-gene downregulation interactions. To our knowledge, this is the first study to systematically evaluate the biological relevance, limitations, and interpretability of proteomization models, establishing Proteomizer as a state-of-the-art tool for multiomic integration and hypothesis generation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/660946v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@59b64dorg.highwire.dtl.DTLVardef@1cf82a4org.highwire.dtl.DTLVardef@89fdb6org.highwire.dtl.DTLVardef@16fe109_HPS_FORMAT_FIGEXP M_FIG C_FIG miRNA: microRNA; Mx: miRNome; MS: mass spectrometry; PTM: post-translational modifications; Px: proteome; Px: predicted proteome; r: Pearson correlation coefficient; RBP: RNA binding proteins; ROC-AUC: area under the receiver operating characteristic curve; RPPA: reverse-phase protein array; TF: transcription factors; Tx: transcriptome; XAI: explainable artificial intelligence.

bioinformatics↗

bioRxiv2025

GhostBuster: A Deep-Learning-based, Literature-Unbiased Gene Prioritization Tool for Gene Annotation Prediction

All genes are not equal before literature. Despite the explosion of genomic data, a significant proportion of human protein-coding genes remain poorly characterized ("ghost genes"). Due to sociological dynamics in research, scientific literature disproportionately focuses on already well-annotated genes, reinforcing existing biases (bandwagon effect). This literature bias often permeates machine learning (ML) models trained on gene annotation tasks, leading to predictions that favor well-studied genes. Consequently, standard ML performance metrics may overestimate biological relevance by overfitting literature-derived patterns. To address this challenge, we developed GhostBuster, an encoder-decoder ML platform designed to predict gene functions, disease associations and interactions while minimizing literature bias. We first compared the impact of biased (Gene Ontology) versus unbiased training datasets (LINCS, TCGA, STRING). While literature-biased sources yielded higher ML metrics, they also amplified bias by prioritizing well-characterized genes. In contrast, models trained on unbiased datasets were 2-3x more effective at identifying recently discovered gene annotations. Notably, one of the unbiased channels (TCGA), combined minimal amounts of literature bias with robust performance, at a test ROC-AUC of 0.8-0.95. We demonstrate that GhostBuster can be applied to predict novel gene functions, refine pathway memberships, and prioritize intergenic GWAS hits. As the first ML framework explicitly designed to counteract literature bias, GhostBuster offers a powerful tool for uncovering the roles of understudied genes in cellular function, disease, and molecular networks. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/660948v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@5318eforg.highwire.dtl.DTLVardef@18a2f99org.highwire.dtl.DTLVardef@13b0b87org.highwire.dtl.DTLVardef@171d3e0_HPS_FORMAT_FIGEXP M_FIG C_FIG GCN: graph convolutional neural network; GeneOntol.: Gene Ontology; LINCS: Library of Integrated Network-Based Cellular Signatures; MLP: multi-layer perceptron (deep learning); PPI: (physical) protein-protein interactions; SVM: support vector machine; TCGA: The Cancer Genome Atlas; y/n: yes or no (binary classification).

bioinformatics↗

bioRxiv2019

In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with Autism risk genes

The thousands of disease risk genes and loci identified through human genetic studies far outstrip our current capacity to systematically study their functions. New experimental approaches are needed for functional investigations of large panels of genes in a biologically relevant context. Here, we developed a scalable genetic screen approach, in vivo Perturb-Seq, and applied this method to the functional evaluation of 35 autism spectrum disorder (ASD) de novo loss-of-function risk genes. Using CRISPR-Cas9, we introduced frameshift mutations in these risk genes in pools, within the developing brain in utero, and then performed single-cell RNA-Seq in the postnatal brain. We identified cell type-specific gene signatures from both neuronal and glial cell classes that are affected by genetic perturbations, and pointed at elements of both convergent and divergent cellular effects across this cohort of ASD risk genes. In vivo Perturb-Seq pioneers a systems genetics approach to investigate at scale how diverse mutations affect cell types and states in the biologically relevant context of the developing organism.

genomics↗