Biology subjects

Baek, B.

Publications and source records attributed to Baek, B..

4 recordsLinked to original sources

bioRxiv2026

PINT: Pathway-pathway interactions for predicting interpretable clinical outcomes from gene expression

MotivationDisease mechanisms emerge from the coordinated activity of multiple biological pathways, rather than from individual pathways acting in isolation. Existing pathway-based deep learning models, however, treat pathways as independent entities, aggregating their representations through fully connected layers that disregard inter-pathway relationships. This architectural limitation overlooks an important dimension of disease biology, potentially constraining both predictive performance and the capacity to generate biologically meaningful interpretations. ResultsWe introduce a pathway-based attentive interpretability model, named PINT, that models interactions among pathways through a self-attention mechanism from gene expression data. An attention-based pooling layer further identifies patient-specific pathway contributions to the final prediction. Evaluation across five TCGA cancer datasets demonstrated that PINT consistently outperformed benchmark models in survival analysis. More importantly, PINT identifies pathways significantly associated with survival as well as reveals biologically meaningful interactions among pathways. In the BRCA dataset, PINT identified significant pathways, pathway-pathway interactions, and gene-level contributions within pathways for individual patients, most of which were supported by existing literature. Specifically, the RAS signaling pathway emerged as significantly associated with patient survival, and the learned interaction scores recovered known relationships between RAS signaling and several regulatory pathways, including cAMP, TNF, and Rap1 signaling. Availability and implementationThe source code and data are available at https://github.com/datax-lab/PINT.

bioinformatics↗

bioRxiv2026

Knowledge-Guided Learning with Curated Prior Genetic Biomarkers for Robust Model Interpretation

MotivationKnowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. ResultsIn this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasts. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementationThe open-source is publicly available at: https://github.com/datax-lab/BioExPL.

bioinformatics↗

bioRxiv2026

PIMO: Pathway-based Interpretable Multi-Omics interactions for multi-omics integration

MotivationModeling inter-omics interactions across multiple molecular levels is critical for deciphering the mechanisms underlying complex diseases. Epigenomic and structural alterations, such as DNA methylation and copy number alterations, modulate gene expression and collectively influence disease progression and patient survival outcomes. Despite advancements in deep learning-based multi-omics analysis, gene-level interactions of inter-omics have been seldom considered, due to combinational complexity and power, which limits interpretability and mechanistic insight. ResultsWe propose a Pathway-based Interpretable deep learning Multi-Omics interaction model, PIMO, that explicitly captures regulatory effects across omics layers. Experiments on multiple TCGA cancer datasets showed that PIMO consistently outperformed state-of-the-art baselines in survival analysis, up to 13% increase in the C-index. PIMO provides biologically interpretable analyses that identify important pathways, genes, and inter-omics interactions with DNA methylation and copy number alterations. Availability and implementationThe source code and data is available at https://github.com/datax-lab/PIMO.

bioinformatics↗

bioRxiv2025

Stochastic LASSO for extremely high-dimensional genomic data

Accurate identification of significant features in high-dimensional data is indispensable in high-throughput genomic analysis and association studies. Least Absolute Shrinkage and Selection Operator (LASSO) and its derivatives have been widely adapted to discover potential biomarkers as a feature selection scheme in various biological systems. Recently, bootstrap-based LASSO models, such as Random LASSO and Hi-LASSO, have been effective solutions for extremely high-dimensional but low sample size (EHDLSS) genomic data. However, the bootstrap-based LASSO models still have several drawbacks, such as multicollinearity within bootstrap samples, missing predictors in draw, and randomness in predictor sampling. To tackle the limitations, we propose a new bootstrap-based LASSO, named Stochastic LASSO, that effectively reduces multicollinearity in bootstrap samples and mitigates randomness in predictor sampling, resulting in remarkably outperforming benchmarks in feature selection and coefficient estimation. Furthermore, Stochastic LASSO provides a two-stage t-test strategy for selecting statistically significant features. The performance of Stochastic LASSO was assessed by comparing the existing benchmark models in extensive simulation experiments. In the simulation experiments, Stochastic LASSO consistently showed significant improvements in performance compared to the state-of-the-art LASSO models for feature selection, coefficient estimation, and robustness. We also applied Stochastic LASSO for the gene expression data of publicly available TCGA cancer datasets and identified statistically significant genes associated with survival month prediction. The source code is publicly available at: https://github.com/datax-lab/StochasticLASSO.

bioinformatics↗