Biology subjects

Parsa, S. P.

Publications and source records attributed to Parsa, S. P..

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

Fairness-aware Supervised Hierarchical Contrastive Semantic Learning for Sexual Dimorphism Analysis

MotivationSexual dimorphism is a fundamental biological determinant driving systematic differences in disease susceptibility, progression, and clinical outcomes. However, current AI-based genomic models often exhibit algorithmic bias and fail to capture these sex-specific mechanisms, creating a critical barrier to unbiased precision medicine. Ensuring fairness in the context of sexual dimorphism requires understanding and addressing the distinct biological mechanisms functioning in each sex, rather than focusing solely on equalizing predictive performance. ResultsWe propose a fairness-aware supervised hierarchical contrastive learning approach, called FairHICON, to discover unbiased sex-common and sex-specific genomic drivers. Evaluations on cancer and asthma transcriptomic datasets demonstrate that FairHICON significantly outperforms state-of-the-art benchmarks, improving predictive performance by up to 9% while effectively reducing the performance gap between male and female cohorts. Furthermore, prognostic validation confirms that the identified sex-specific pathways stratify patient survival significantly better within their corresponding sex groups. This validates FairHICON to elucidate the molecular heterogeneity of sexual dimorphism, advancing inclusive precision medicine. Availability and implementationThe source code and data is available at https://github.com/datax-lab/FairHICON.

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

Prediction of bacterial protein-compound interactions with only positive samples

Prediction of Compound-Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields, including bio-catalysis, drug discovery, and industrial processing. However, current CPI models cannot be applied for bacterial CPI prediction due to the lack of curated negative interaction samples. This paper introduces a novel Positive-Unlabeled (PU) learning framework, named BIN-PU, to address this limitation. BIN-PU generates pseudo positive and negative labels from known positive interaction data, enabling effective training of deep learning models for CPI prediction. We also propose a weighted positive loss function that weights to truly positive samples. We have validated BIN-PU with multiple CPI backbone models, comparing the performance with the existing PU model using bacterial cytochrome P450 (CYP) data. Extensive experiments demonstrate the superiority of BIN-PU over the benchmark model in predicting CPIs with only truly positive samples. Furthermore, we have validated BIN-PU on additional bacterial proteins obtained from literature review, human CYP datasets, and uncurated data for its reproducibility. We have also validated the CPI prediction for the uncurated CYP data with biological and biophysical experiments. BIN-PU represents a significant advancement in CPI prediction for bacterial proteins, opening new possibilities for improving predictive models in related biological interaction tasks.

bioinformatics↗