arXiv subjects

Augustin Luna

Publications and source records attributed to Augustin Luna.

6 recordsLinked to original sources

arXiv2026

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug representations, without explicitly modeling the molecular changes expected under treatment. We propose PerturbRx, a treatment-conditioned representation learning framework that learns intervention-induced latent transitions and uses them as patient-drug response features. PerturbRx trains a drug- and dose-conditioned transition predictor from context-matched but cell-unpaired control and treated single-cell populations, then freezes and transfers the predictor to pretreatment patient profiles without requiring post-treatment measurements. The transition is combined with patient and drug representations to predict response. Across TCGA and patient-derived xenograft benchmarks, PerturbRx achieves the strongest aggregate predictive performance among the evaluated methods. These results support perturbation-pretrained latent transitions as useful representations for patient-level drug-response prediction.

q-bio.QM↗

arXiv2025

User-Guided Force-Directed Graph Layout

Visual analysis of relational data is essential for many real-world analytics tasks, with layout quality being key to interpretability. However, existing layout algorithms often require users to navigate complex parameters to express their intent. We present a user-guided force-directed layout approach that enables intuitive control through freehand sketching. Our method uses classical image analysis techniques to extract structural information from sketches, which is then used to generate positional constraints that guide the layout process. We evaluate the approach on various real and synthetic graphs ranging from small to medium scale, demonstrating its ability to produce layouts aligned with user expectations. An implementation of our method along with documentation and a demo page is freely available on GitHub at https://github.com/sciluna/uggly.

cs.GR↗

arXiv2025

GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction

Explainability is necessary for many tasks in biomedical research. Recent explainability methods have focused on attention, gradient, and Shapley value. These do not handle data with strong associated prior knowledge and fail to constrain explainability results based on known relationships between predictive features. We propose GraphPINE, a graph neural network (GNN) architecture leveraging domain-specific prior knowledge to initialize node importance optimized during training for drug response prediction. Typically, a manual post-prediction step examines literature (i.e., prior knowledge) to understand returned predictive features. While node importance can be obtained for gradient and attention after prediction, node importance from these methods lacks complementary prior knowledge; GraphPINE seeks to overcome this limitation. GraphPINE differs from other GNN gating methods by utilizing an LSTM-like sequential format. We introduce an importance propagation layer that unifies 1) updates for feature matrix and node importance and 2) uses GNN-based graph propagation of feature values. This initialization and updating mechanism allows for informed feature learning and improved graph representation. We apply GraphPINE to cancer drug response prediction using drug screening and gene data collected for over 5,000 gene nodes included in a gene-gene graph with a drug-target interaction (DTI) graph for initial importance. The gene-gene graph and DTIs were obtained from curated sources and weighted by article count discussing relationships between drugs and genes. GraphPINE achieves a PR-AUC of 0.894 and ROC-AUC of 0.796 across 952 drugs. Code is available at https://anonymous.4open.science/r/GraphPINE-40DE.

cs.LG↗

arXiv2024

DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment

Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literature. This evidence can be incomplete or conflicting. DrugAgent is a large language model (LLM)-based multi-agent system focused on DTI evidence integration that integrates outputs from machine learning, knowledge graph, and retrieval-augmented generation (RAG) agents. DrugAgent converts agent outputs into interpretable representations, then summarizes conflict across the evidence. We evaluated DrugAgent on kinase screening data of 900 pairs spanning 178 kinases and 42 inhibitors, and an androgen receptor antagonist screening benchmark. On the kinase dataset, LLM-as-a-Judge evaluation indicated outputs were faithful to input evidence in 98.8% of cases. Biological plausibility of returned summarization was high (scores 3-4 out of 5) across ground-truth classes: 79% of Weak activity labels cases (81% for Moderate/77% Strong); Strong cases received higher scores than Weak/Moderate. Label stability showed 98% agreement across runs. Results on the antagonist benchmark were consistent with the kinase dataset. Retrieved literature provided the greatest benefit when direct drug-target evidence was available, highlighting the importance of evidence availability for RAG-based integration. DrugAgent provides heterogeneous evidence-grounded DTI assessment, complementing standalone DTI prediction. We provide strategies to model agreement, conflict, and uncertainty in biomedical evidence integration. Code: https://github.com/sciluna/DrugAgent.

cs.AI↗

arXiv2024

drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present drGT, a heterogeneous graph deep learning model over drugs, genes, and cell lines that couples prediction with mechanism-oriented interpretability via attention coefficients (ACs). We assess both predictive generalization (random, unseen-drug, unseen-cell, and zero-shot splits) and biological plausibility (use of text-mined PubMed gene-drug co-mentions and comparison to a structure-based DTI predictor) on GDSC, NCI60, and CTRP datasets. Across benchmarks, drGT consistently delivers top regression performance while maintaining competitive classification accuracy for drug sensitivity. Under random 5-fold cross-validation, drGT attains an AUROC of up to 0.945 (3rd overall) and an $R^2$ up to 0.690, outperforming all baselines on regression. In leave-one-out tests for unseen cell lines and drugs, drGT achieves AUROCs of 0.706 and 0.844, and $R^2$ values of 0.692 and 0.022, the only model yielding positive $R^2$ for unseen drugs. In zero-shot prediction, drGT achieves an AUROC of 0.786 and a regression $R^2$ of 0.334, both representing the highest scores among all models. For interpretability, AC-derived drug-gene links recover known biology: among 976 drugs with known DTIs, 36.9% of predicted links match established DTIs, and 63.7% are supported by either PubMed abstracts or a structure-based predictive model. Enrichment analyses of AC-prioritized genes reveal drug-perturbed biological processes, providing pathway-level explanations. drGT advances predictive generalization and mechanism-centered interpretability, offering state-of-the-art regression accuracy and literature-supported biological hypotheses that demonstrate the use of graph learning from heterogeneous input data for biological discovery. Code: https://github.com/sciluna/drGT

cs.LG↗

arXiv2014

MIMTool: A Tool for Drawing Molecular Interaction Maps

Background: To understand protein function, it is important to study protein- protein interaction networks. These networks can be represented in network diagrams called protein interaction maps that can lead to better understanding by visualization. We address the problem of drawing of protein interactions in Kohn's Molecular Interaction Map (MIM) notation. Even though there are some existing tools for graphical visualization of protein interactions in general, there is no tool that can draw protein interactions with MIM notation with full support. Results: MIMTool was developed for drawing protein interaction maps in Kohn's MIM notation. MIMTool was developed using the Qt toolkit libraries and introduces several unique features such as full interactivity, object dragging, ability to export files in MIMML, SBML and line drawing with automatic bending and crossover minimization, which are not available in other diagram editors. MIMTool also has a unique orthogonal edge drawing method that is both easy and more flexible than other orthogonal drawing methods present in other interaction drawing tools. Conclusions: MIMTool facilitates faster drawing, updating and exchanging of MIMs. Among its several features, it also includes a semi-automatic drawing algorithm that makes use of shortest path algorithm for constructing lines with small number of bends and crossings. MIMTool contributes a much needed software tool that was missing and will facilitate wider adoption of Kohn's MIM notation.

cs.CE↗