Biology subjects

Oh, S.-Y.

Publications and source records attributed to Oh, S.-Y..

3 recordsLinked to original sources

bioRxiv2026

fastACCORD enables ultrahigh-dimensional partial correlation modeling for multi-omic data integration

Gene co-expression networks reflect transcription factor-associated regulation together with epigenetic influences such as DNA methylation, chromatin states, and histone modifications. To distinguish gene-gene dependencies that persist after accounting for methylation covariation, large-scale statistical inference conditioning on hundreds of thousands of molecular features is necessary, but the task remains computationally intractable for conventional Gaussian graphical modelling approaches. Here we present fastACCORD, a scalable computational framework for ultrahigh-dimensional partial correlation modeling. fastACCORD combines row-separable optimization, {ell}2 stabilization, and a semismooth Newton solver in a PyTorch implementation for CPU and CUDA-enabled GPU hardware. We applied it to matched transcriptomic and methylomic profiles from 16 TCGA cancer types and obtained joint networks containing >300,000 molecular features per cancer. The resulting multi-omic networks revealed cancer-specific methylation-expression dependencies, including recurrent methylation-associated expression repression of metabolic genes. Methylation-adjusted gene co-expression networks were sparser than networks estimated from mRNA data alone, but more enriched for ChIP-seq-supported TF-target relationships and curated co-regulon annotations. TF-target subnetworks further revealed cancer-specific architectures consistent with lineage identity, oncofetal reactivation, and tumor microenvironment-associated programs. Together, these analyses establish fastACCORD as a practical framework for ultrahigh-dimensional multi-omic partial correlation modeling and demonstrate how joint modeling of transcriptomic and epigenomic measurements can refine the interpretation of gene co-expression networks.

bioinformatics↗

bioRxiv2024

Awe is characterized as an ambivalent experience in the human behavior and cortex: integrated virtual reality-electroencephalogram study

Ambivalent feelings are a defining feature of awe, which has been understood as a possible source of its psychosocial benefits. However, due to the conventional unidimensional model of affective valence, behavior and neural representation of ambivalent feelings during awe remain elusive. To address this gap, we combined awe-inducing virtual reality clips, electroencephalogram, and a deep learning-based dimensionality reduction technique (N = 43). Behaviorally, awe ratings were precisely predicted by the duration and intensity of ambivalent feelings, not by single valence-related metrics. In the electrophysiological analysis, we identified latent neural space for each participant sharing valence representation structures across individuals and stimuli. In these spaces, ambivalent feelings during awe were distinctly represented from positive and negative ones, and the variability in their distinctiveness specifically predicted awe ratings. Additionally, frontal delta oscillations mainly engaged in differentiating valence representations. Our findings demonstrate that awe is fundamentally an ambivalent experience reflected in both behavior and electrophysiological activities. This work provides a new framework for understanding complex emotions and their neural underpinnings, with potential implications for affective neuroscience and relevant fields.

neuroscience↗

bioRxiv2020

RIOK2 drives glioblastoma cell growth and proliferation by modulating MYC through the RNA-binding protein IMP3

Glioblastoma (GBM) is the most common primary malignant brain tumor and is resistant to current therapies. Here, using a combination of proteomic and genetic approaches in both Drosophila and human GBM models, we identify novel signaling effectors that act downstream of EGFR and PI3K signaling pathways, which drive GBM. Our results indicate that RIOK2, an atypical serine/threonine kinase, forms a complex with the RNA-binding protein, IMP3 (Imp in Drosophila), and that the dRIOK2/RIOK-Imp/IMP3 pathway is necessary for tumorigenesis. Furthermore, our results indicate that RIOK2 catalytic activity is important for neoplasia and recruits TORC2 to phosphorylate IMP3. Finally, the dRIOK2-Imp pathway regulates protein levels of dMyc, a known Imp target mRNA, and these key findings were recapitulated in human GBM models. Collectively, our data indicates that RIOK2 catalytic activity promotes the activation of TORC2 and recruitment of IMP3, which in turn, modulates MYC mRNA and protein levels to promote tumorigenesis.

cancer biology↗