Biology subjects

Sanchez-Valle, J.

Publications and source records attributed to Sanchez-Valle, J..

6 recordsLinked to original sources

bioRxiv2026

Interpretable Forecasting of Kidney Cancer Progression via Generative AI and Symbolic Reasoning

Predicting cancer stage progression from omics data, and deriving molecular insight into the mechanisms driving it, remains a major challenge, owing in part to the lack of adequate longitudinal data and the interpretability limitations of current forecasting models. Large cancer datasets such as TCGA capture patient profiles cross-sectionally rather than longitudinally, complicating timely treatment decisions as tumors become more invasive. Deep neural networks typically used for forecasting, such as LSTMs, compound this problem by remaining largely opaque and offering clinicians no straightforward way to audit their predictions. Clear cell renal cell carcinoma (ccRCC) illustrates the clinical stakes of both challenges. Five-year survival falls from over 94% at stage I to 28% at stage IV, yet early-stage tumors are often managed under active surveillance, a strategy constrained by sparse molecular evidence of progression risk. Detecting progression in time, meanwhile, demands forecasts clinicians can interpret and trust, not black-box predictions. We address both challenges by combining generative and symbolic AI: a Variational Autoencoder trained on bulk RNA-Seq profiles of 530 TCGA ccRCC patients generates synthetic pseudo-time trajectories that overcome the absence of longitudinal data, while a symbolic rule-induction framework (ASAL) learns finite-state automata from these trajectories, encoding stage transition as human-readable Boolean conditions over gene expression, which a complex event forecasting system (Wayeb) converts into probabilistic forecasts of stage advancement. An independent XGBoost classifier trained on real patients (F1 score = 0.71-0.81) shows a gradual early-to-late probability shift along the synthetic trajectories, absent in non-progressing control trajectories. Pathway enrichment of those trajectories reveals stage-dependent changes in established kidney cancer-related processes, including the TCA cycle and DNA repair. Finally, our symbolic forecaster nearly matches an LSTM baseline (macro F1 = 0.928 vs. 0.964), while additionally offering an inspectable rule set and a probability distribution over transition timing rather than a single opaque score. This work shows that generative and symbolic AI, paired together, can turn cross-sectional cohorts into a transparent, forecast-oriented framework for modeling disease progression, demonstrated here in ccRCC.

bioinformatics↗

bioRxiv2026

A Genetic Atlas of Direct and Inverse Neuropsychiatric-Cancer Comorbidity

Direct and inverse comorbidities between neuropsychiatric disorders and cancer are increasingly recognised as important features of the nervous system-cancer relationship, yet the inherited genetic architecture underlying these patterns remains poorly understood. Here, we analysed pairwise genetic correlations across 35 diseases represented by 115 GWAS datasets, including 9 psychiatric disorders, 10 neurological diseases and 16 cancers, using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL), complemented by meta-analysis, subtype-resolved analyses, local covariance mapping and multi-omic benchmarking. Genetic correlations were predominantly positive and strongest within disease categories, whereas cancer-neurological pairs showed the weakest overall genetic affinity. Meta-analysis and subtype resolution uncovered associations obscured in aggregate analyses, including opposing correlations between familial and late-onset Alzheimers disease and lung cancer, revealing subtype-dependent neuro-oncological biology. Local analyses identified recurrent genomic loci where direct comorbidities are consistent with shared inflammatory, interferon, survival and tissue-remodelling programs, whereas inverse comorbidities suggest competing demands on apoptotic regulation, immune tone and stress-response calibration between neuronal and tumour-cell states. Together, these findings provide a genome-scale genetic framework for neuropsychiatric-cancer comorbidity and identify shared inherited biological programs as candidates for mechanistic investigation and therapeutic translation.

genomics↗

bioRxiv2026

EBEx: an Ensemble-Based Explainable Framework for Gene Calling in Heterogeneous Diseases

Complex and clinically heterogeneous diseases pose significant challenges for gene prioritisation and patient stratification, as relevant genes often show weak or context-specific signals and transcriptomic datasets are limited in size. These limitations hinder the discovery of robust molecular signatures using traditional case-control approaches and motivate computational pipelines capable of capturing molecular diversity. Here, we present an explainable ensemble-based AI pipeline to prioritise disease-relevant genes from transcriptomic data, using Chronic Obstructive Pulmonary Disease (COPD) as a use case. To retain biologically relevant interactors obscured by molecular heterogeneity, the framework integrates data-driven signals with curated COPD-related gene sets, further expanded through network-based prioritisation and supported by molecular interactions. Gene relevance is evaluated via aggregated explainability scores across multiple classifier configurations to ensure robust candidate selection. The final set comprised < 8% of evaluated genes, [~] 62% arising from network-based expansion, substantially reducing dimensionality while preserving biological heterogeneity. Beyond case-control classification, the approach identified candidate genes and molecular subgroups associated with specific clinical features, capturing patient-level heterogeneity. The prioritised genes recapitulated key disease-related processes, including immune responses and extracellular matrix degradation, and highlighted additional associations like the enrichment of the IL-4 and IL-13 signalling pathway, which is of clinical interest given ongoing biologic developments targeting these axes. Our pipeline outperformed existing methods in discriminating COPD from controls, and the final gene list was validated in independent cohorts. Implemented as a scalable and reusable R package, this framework facilitates the study of molecular heterogeneity in complex diseases like COPD, supporting advances in diagnosis and precision medicine. Availability and implementationEBEx code and tutorials can be found in: https://iposelag.github.io/EBEx/

bioinformatics↗

bioRxiv2025

Sex-specific transcriptome similarity networks elucidate comorbidity relationships

Humans present sex-driven biological differences. Consequently, the prevalence of analyzing specific diseases and comorbidities differs between the sexes, directly impacting patients management and treatment. Despite its relevance and the growing evidence of said differences across numerous diseases (with 4,370 PubMed results published within the past year), knowledge at the comorbidity level remains limited. In fact, to date, no study has attempted to identify the biological processes altered differently in women and men, promoting differences in comorbidities. To shed light on this problem, we analyze expression data for more than 100 diseases from public repositories, analyzing each sex independently. We calculate similarities between differential expression profiles by disease pairs and find that 13-16% of transcriptomically similar disease pairs are sex-specific. By comparing these results with epidemiological evidence, we recapitulate 53-60% of known comorbidities distinctly described for men and women, finding sex-specific transcriptomic similarities between sex-specific comorbid diseases. The analysis of shared underlying pathways shows that diseases can co-occur in men and women by altering alternative biological processes. Finally, we identify different drugs differentially associated with comorbid diseases depending on patients sex, highlighting the need to consider this relevant variable in the administration of drugs due to their possible influence on comorbidities.

bioinformatics↗

bioRxiv2019

Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

Matrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology.\n\nWe here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimers disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities.\n\nTo this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through a pancancer analysis. Additionally, new candidate molecular players, such as Estrogen Receptor (ER), CDH1 and HDAC, are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, suggesting the existence of heterogeneity across patients also in the context of inverse comorbidity.

systems biology↗

bioRxiv2018

Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profiles

Comorbidity is an impactful medical problem that is attracting increasing attention in healthcare and biomedical research. However, little is known about the molecular processes leading to the development of a specific disease in patients affected by other conditions. We present a disease interaction network inferred from similarities in patients molecular profiles, which significantly recapitulates epidemiologically documented comorbidities, providing the basis for their interpretation at a molecular level. Furthermore, expanding on the analysis of subgroups of patients with similar molecular profiles, our approach discovers comorbidity relations not previously described, implicates distinct genes in such relations, and identifies drugs whose side effects are potentially associated to the observed comorbidities.

bioinformatics↗