arXiv subjects

Zeynep H. Gümüş

Publications and source records attributed to Zeynep H. Gümüş.

4 recordsLinked to original sources

arXiv2026

ClusterChirp: Scalable Interactive Exploration of Omics Data with Natural Language-Guided Analysis

High-dimensional omics datasets are routinely visualized as heatmaps, where color intensities reveal co-expression patterns and correlations. However, modern omics technologies increasingly generate matrices so large that existing visual exploration tools require down-sampling or filtering, causing loss of biologically important patterns. Additional barriers arise from tools that require command-line expertise, or fragmented workflows for downstream biological interpretation. We present ClusterChirp, a web-based platform for real-time exploration of large-scale data matrices. The platform combines GPU-accelerated rendering and parallelized hierarchical clustering using multiple CPU cores. Built on deck.gl and multi-threaded clustering algorithms, ClusterChirp supports on-the-fly clustering, multi-metric sorting, feature search and interactive visualization controls within a single interface. Uniquely, a natural language interface powered by a Large Language Model allows users to perform complex operations and build reproducible workflows through conversational commands. ClusterChirp further enables within-cluster correlation network analysis in 2D or 3D, and integrates functional enrichment through biological knowledge bases. Developed with iterative user feedback and adhering to FAIR4S principles, ClusterChirp enables users to extract insights from high-dimensional omics data with unprecedented ease and speed. It is freely available at clusterchirp.mssm.edu without login and is also distributed as a Dockerized application at ghcr.io/gumuslab/clusterchirp.

q-bio.GN↗

arXiv2025

HEMERA: A Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data

Lung cancer (LC) is the third most common cancer and the leading cause of cancer deaths in the US. Although smoking is the primary risk factor, the occurrence of LC in never-smokers and familial aggregation studies highlight a genetic component. Genetic biomarkers identified through genome-wide association studies (GWAS) are promising tools for assessing LC risk. We introduce HEMERA (Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data), a new framework that applies explainable transformer-based deep learning to GWAS data of single nucleotide polymorphisms (SNPs) for predicting LC risk. Unlike prior approaches, HEMERA directly processes raw genotype data without clinical covariates, introducing additive positional encodings, neural genotype embeddings, and refined variant filtering. A post hoc explainability module based on Layer-wise Integrated Gradients enables attribution of model predictions to specific SNPs, aligning strongly with known LC risk loci. Trained on data from 27,254 Million Veteran Program participants, HEMERA achieved >99% AUC (area under receiver characteristics) score. These findings support transparent, hypothesis-generating models for personalized LC risk assessment and early intervention.

cs.LG↗

arXiv2024

PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/

q-bio.MN↗

arXiv2018

Developing Design Guidelines for Precision Oncology Reports

Precision oncology tests that profile tumors to identify clinically actionable targets have rapidly entered clinical practice. Effective visual presentation of the results of these tests is crucial in accurate clinical decision-making. In current practice, these results are typically delivered to oncologists as static prints, who then incorporate them into their clinical decision-making process. However, due to a lack of guidelines for standardization, different vendors use different report formats. There is very little known on the effectiveness of these report formats or the criteria necessary to improve them. In this study, we have aimed to identify both the tasks and the needs of oncologists from precision oncology report design and then to improve the designs based on these findings. To this end, we report results from multiple interviews and a survey study (n=32) conducted with practicing oncologists. Based on these results, we compiled a set of design criteria for precision oncology reports and developed a prototype report design using these criteria, along with feedback from oncologists.

q-bio.TO↗