Biology subjects

Gamsiz Uzun, E. D.

Publications and source records attributed to Gamsiz Uzun, E. D..

3 recordsLinked to original sources

bioRxiv2026

A Multimodal Neural Network Model for Early Recurrence Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is the most common primary lung cancer worldwide. Despite advancements in early detection and treatment, up to 39% of patients develop recurrent tumors following complete resection. Currently, no widely available models exist for reliably predicting early recurrence of LUAD, which is a significant prognostic factor of post-recurrence survival. Models leveraging deep learning (DL) techniques have demonstrated notable utility in cancer recurrence prediction, particularly when used in combination with both clinical and genomic data. We developed a DL-based model, Predicting Lung Adenocarcinoma recurrence via Selective Multimodal Attention (PLASMA), to predict early recurrence using clinical, mRNA expression, and mutation data from patients with primary stage I-III LUAD. Trained on The Cancer Genome Atlas (TCGA) dataset, PLASMA outperformed traditional machine learning models in predicting early recurrence in both the TCGA test set and an external validation set (TRACERx Lung), achieving area under the receiver operating characteristic curve (AUROC) scores of 85.0% and 76.5%, respectively. Our results support the potential of multimodal DL for early LUAD recurrence prediction and risk stratification.

bioinformatics↗

bioRxiv2024

LUNAR: A Deep Learning Model to Predict Glioma Recurrence Using Integrated Genomic and Clinical Data

Gliomas account for approximately 25.5% of all primary brain and central nervous system tumors, with a striking 80.8% of these being malignant. The prognosis varies significantly; low-grade gliomas (LGGs) can exhibit 5-year survival rates of up to 80%, while higher-grade gliomas (HGGs) often see rates below 5%. Recurrence is a common challenge, occurring in 52%-62% of LGGs and 90% of HGGs, complicating clinical management and treatment planning. Currently, no widely available models exist for predicting glioma recurrence, which is critical for optimizing patient outcomes. Machine learning (ML) and deep learning (DL) techniques have shown promise in predicting recurrence for various cancers, most using Electronic Health Records (EHR). This study introduces gLioma recUrreNce Attention-based classifieR (LUNAR), a DL-based model to predict early versus late glioma recurrence by integrating clinical, genomic, and mRNA expression data from patients with primary grade II-IV gliomas. The data was obtained from The Cancer Genome Atlas and the Glioma Longitudinal Analysis Consortium (GLASS).

bioinformatics↗

bioRxiv2020

VarStack: a Web Tool for Data Retrieval to Interpret Somatic Variants in Cancer

Background and objectiveAdvances in tumor genome sequencing created an urgent need for bioinformatics tools to support the interpretation of the clinical significance of the variants detected. VarStack is a web tool which is a base to retrieve somatic variant data in cancer from existing databases. MethodsVarStack incorporates data from several publicly available databases and presents them with an easy-to-navigate user-interface. It currently supports data from the Catalogue of Somatic Mutations in Cancer (COSMIC), gnomAD, cBioPortal, ClinVar, OncoKB and UCSC Genome browser. It retrieves the data from these databases and returns back to the user in a fraction of the time it would take to manually navigate each site independently. ResultsUsers submit a variant with gene symbol, peptide change, and coding sequence change. They may select a variety of tumor specific studies in cBioportal to search through in addition to their original query. The results from the databases are presented in tabs. Users can export the results as a CSV file. VarStack also has the batch search feature in which user submits a list of variants and download a CSV file with the data from the databases. With the batch search and data download options users can easily incorporate VarStack into their workflow or tools. VarStack saves time by providing variant data to the user from multiple databases in an easy-to-export and interpretable format. AvailabilityVarStack is freely available under https://varstack.brown.edu.

bioinformatics↗