Biology subjects

Harmanci, A.

Publications and source records attributed to Harmanci, A..

9 recordsLinked to original sources

bioRxiv2026

RDEmbed: A Sentence-Embedding Framework for Rare Disease Phenotype Representation and Phenotypic Similarity Analysis

Accurate phenotypic characterization of patients with rare diseases is essential for improving diagnosis, patient matchmaking, and gene discovery. Conventional ontology driven approaches based on the Human Phenotype Ontology (HPO) enable structured phenotype comparison but often limited by the incomplete annotations and reduced ability to capture the contextual information contained in free-text clinical narratives. We present RDEmbed, a sentence embedding framework that learns patient phenotype representations directly from rare disease case reports for similarity analysis. RDEmbed encodes narrative case reports into high-dimensional vectors to quantify phenotypic similarity. Unlike ontology-based approaches that rely on manually curated HPO annotations, RDEmbed encodes narrative case reports into dense semantic embeddings that capture complex phenotypic relationships from unstructured text. We further introduced retrieval-augmented embedding refinement (RAER), which enhances embedding representations by incorporating external domain knowledge at inference time without task-specific fine-tuning. RDEmbed was evaluated against HPO-based similarity methods (OWLSim, simGIC) using simulated datasets, published rare disease case reports and a curated case study dataset comprising 156 patient abstracts presenting six rare disease groups. Across the real world datasets and curated cohort. RDEmbed consistently outperformed ontology based approaches, improving AUROC and AUPRC by more than 5%. Embedding-based representations also produced more distinct disease-specific clustering in t-SNE visualizations and identified twice as many true positive rare disease pairs as ontology based methods (8 versus 4). Incorporating RAER further improved precision while maintaining high sensitivity compared with zero-shot embeddings. These results demonstrate that sentence embeddings provide an effective alternative to ontology-based phenotype representations by learning directly from free-text clinical narratives. RDEmbed enables more accurate phenotypic similarity analysis while reducing dependence on manually curated HPO annotations and offers a scalable framework for rare disease diagnosis, patient matchmaking, and gene discovery. RDEmbed is publicly available at https://github.com/xhwang2017/RDEmbed.

bioinformatics↗

bioRxiv2026

GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers Prediction

Gliomas are aggressive primary brain tumors that necessitate critical molecular biomarker predictions for optimal clinical decision-making. Traditional assessment relies on surgical tumor specimens analysis, which carries procedural risks and sampling bias due to tumor heterogeneity. Existing deep learning methods for non-invasive prediction lack real-time applicability, remain resource-intensive, and are frequently trained on narrowly represented datasets. We present GlioVision, a framework built on the MONAI library to process multimodal data, including glioma MRI and molecular labels, to predict and identify, non-invasively, four major glioma molecular biomarkers: IDH mutation, 1p/19q co-deletion, MGMT methylation, and WHO grade. The core architecture comprises Spatially and Channel-wise Recalibrated 3D DenseNet (SCRU-DenseNet), which utilizes a computational attention gate and an Adaptive Contrast-Specific Processing Stream (ACPS) to tackle multi-site, heterogeneous datasets. We introduced the Confidence-Filtered Predictive Manifold (CFPM) to manage uncertainty by excluding predictions with low confidence. GlioVision is trained and validated on the largest multi-cohort datasets, achieving strong biomarker prediction with AUCs of (IDH 0.94, 1p/19q 0.87, MGMT 0.86, WHO grades 0.92), supporting molecularly defined glioma diagnosis under the WHO 2021 classification guidelines. Finally, we provide a Differential Training Integrity Assessment (DTI-A) to analyze routes of MRI data privacy protections through model obfuscation. Our results advance the codebase, model release, and leakage considerations around MRI data analysis literature.

neuroscience↗

bioRxiv2025

Mutant IDH impairs chromatin binding by PDGFB to promote chromosome instability

Non-canonical roles for growth factors in the nucleus have been previously described, but their mechanism of action and biological roles remain enigmatic. Platelet-derived growth factor B (PDGFB) can drive formation of low-grade glioma and here we show that it localizes to the nucleus of human glioma cells where it binds chromatin to preserve genome stability and cell lineage. Failure of PDGFB to localize to the nucleus leads to chromosomal abnormalities, aberrant heterochromatin architecture and accelerated tumorigenesis. Furthermore, nuclear localization of PDGFB is reliant upon the expression levels and mutation status of isocitrate dehydrogenase (IDH). Unexpectedly, we identified macrophages as the predominant source of PDGFB in human, finding that immune-derived PDGFB can localize to the nucleus of glioma cells. Collectively, these studies show that immune derived PDGFB enters the nucleus of glioma cells to maintain genomic stability, while identifying a new mechanism by which IDH mutations promote gliomagenesis.

cancer biology↗

bioRxiv2023

FedGMMAT: Federated Generalized Linear Mixed Model Association Tests

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for arbitrary fixed and random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by homomorphic encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

bioinformatics↗

bioRxiv2022

Epigenetic and Signaling Determinants of Blood-Brain Barrier-Regulatory Genetic Networks

The blood-brain barrier (BBB) controls the movement of molecules into and out of the central nervous system (CNS). Since a functional BBB forms by mouse embryonic day E15.5, we reasoned that gene cohorts expressed in CNS endothelial cells (EC) at E13.5 contribute to BBB formation. In contrast, adult gene signatures reflect BBB maintenance mechanisms. Supporting this hypothesis, transcriptomic analysis revealed distinct cohorts of EC genes involved in BBB formation and maintenance. Here, we demonstrate that epigenetic regulators histone deacetylase 2 (HDAC2) and polycomb repressive complex 2 (PRC2) control EC gene expression for BBB development and prevent Wnt/{beta}-catenin (Wnt) target genes from being expressed in adult CNS ECs. Low Wnt activity during development modifies BBB genes epigenetically for the formation of functional BBB. As a Class-I HDAC inhibitor induces adult CNS ECs to regain Wnt activity and BBB genetic signatures that support BBB formation, our results inform strategies to promote BBB repair.

neuroscience↗

bioRxiv2019

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

A rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a \"pathogenicity score\" to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

bioinformatics↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

bioRxiv2018

Sensitive information leakage from functional genomics data: Theoretical quantifications & practical file formats for privacy preservation

The generation of functional genomics datasets is surging, as they provide insight into gene regulation and organismal phenotypes (e.g., genes upregulated in cancer). The intention of functional genomics experiments is not necessarily to study genetic variants, yet they pose privacy concerns due to their use of next-generation sequencing. Moreover, there is a great incentive to share raw reads for better analyses and general research reproducibility. Thus, we need new modes of sharing beyond traditional controlled-access models. Here, we develop a data-sanitization procedure allowing raw functional genomics reads to be shared while minimizing privacy leakage, thus enabling principled privacy-utility trade-offs. It works with traditional Illumina-based assays and newer technologies such as 10x single-cell RNA-sequencing. The procedure depends on quantifying the privacy leakage in reads by statistically linking study participants to known individuals. We carried out these linkages using data from highly accurate reference genomes and more realistic environmental samples.

bioinformatics↗

bioRxiv2018

Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed \"putative passengers\") are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

genomics↗