Namshik Han

Non-exonic read content reorganises lncRNA co-expression networks in renal and other cancers

The paper shows that non-exonic read fractions reorganize lncRNA co-expression networks in renal and other cancers, with varying impacts across cancer types.

Malignant and stromal partial EMT programmes and cetuximab outcome in head and neck cancer

The paper identifies malignant and stromal partial EMT programs in head and neck cancer and shows that malignant measures correlate with better cetuximab outcomes.

An amyloid-β/APP regulatory layer in human brown adipocyte recruitment identifies repurposable drugs for thermogenic fat expansion

BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration

BRIDGE-AD integrates multimodal AD datasets to prioritize disease effectors, identifying 19 functional clusters and validating SCARB2 as a novel candidate.

Carcinogen-induced preinvasive human squamous lung cancer is characterised by a novel atypical basal cell state and presents opportunities for chemoprevention

This study develops a human model of early squamous lung cancer induced by a carcinogen, identifies a novel atypical basal cell state, and shows that capivasertib prevents and reverses the abnormal phenotype.

A network medicine framework for multi-modal data integration in therapeutic target discovery

This paper presents a network medicine framework integrating multi-modal data to identify therapeutic targets in clear cell renal cell carcinoma, recovering established targets and predicting five candidates, with ENO2 inhibition showing the strongest anti-tumour effect.

Single-cell foundation models reveal context-sensitive cancer programmes under subtype shift

The paper fine-tunes two scFMs on cancer subtypes and shows they outperform LightGBM on out-of-domain subtypes, with context-sensitive adaptation supported by interpretability analysis.

Quantitative Optimization of Sensitivity and Specificity in Targeted and Whole-Exome Sequencing Using Reference-Standard DNA Mixtures

The paper optimizes sensitivity and specificity in targeted and whole-exome sequencing using reference-standard DNA mixtures, identifying optimal pipelines and VAF thresholds to reduce false positives and improve detection.

Utilising multi-modal data-driven network analysis to identify monotherapy and combinational therapy targets in SOX2-dependent squamous cell lung cancer

The paper develops a computational pipeline to identify drug targets for SOX2-dependent lung cancer using multi-modal data and validates them in vitro.

Evaluation of false positive and false negative errors in targeted next generation sequencing

This study evaluates false positive and false negative errors in targeted NGS by analyzing reference-standard samples from certified providers.

Compensatory tRNA Modification by DUS3L Confers Resistance to METTL1 Loss in Oesophageal Cancer

The paper identifies a compensatory tRNA modification pathway mediated by DUS3L that allows oesophageal cancer cells to resist METTL1 loss, suggesting DUS3L as a biomarker and therapeutic target.

Multimodal gene and targeted drug therapy for chronic myelogenous leukemia: Computational target analysis and therapeutic validation

This paper develops a multimodal therapy for CML combining computational target analysis with nanoparticle-based drug delivery and validates its efficacy in mouse models.

A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma

This paper develops a machine learning framework for therapeutic target discovery in ccRCC, identifying 16 targets and novel mechanisms with superior efficacy to standard-of-care drugs.

Combined stem cell and predictive models reveal flavin cofactors as targets in metabolic liver dysfunction

The paper combines stem cell models and computational predictions to identify flavin cofactors as therapeutic targets for metabolic liver dysfunction.

Downregulation of dystrophin expression occurs across diverse tumors, correlates with the age of onset, staging, and reduced survival of patients.

This study shows that dystrophin expression is downregulated in many tumors, linked to advanced stages, older age of onset, and poorer survival.

scRNA-Seq-based drug repurposing targeting idiopathic pulmonary fibrosis (IPF)

This study uses scRNA-Seq data to identify drug candidates for repurposing in IPF by matching cell transition signatures to drug perturbation signatures.

Predicting genes associated with RNA methylation pathways using machine learning

This study uses machine learning to predict genes associated with RNA methylation pathways in humans using multi-omics data from the Harmonizome database.

dialogi: Utilising NLP with chemical and disease similarities to drive the identification of Drug-Induced Liver Injury literature

The paper presents an automated pipeline using NLP and chemical/disease embeddings to classify DILI-related literature, achieving high F1-scores in distinguishing DILI-positive from negative papers.

DILIc: An AI-based classifier to search for Drug-Induced Liver Injury literature

This paper introduces DILIc, an AI classifier that identifies Drug-Induced Liver Injury literature using NLP and ML, achieving high accuracy in cross-validation and external validation.

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

The paper develops a framework to identify time-concordant gene expression events in DILI using time-resolved transcriptomics and applies it to rat liver data from TG-GATEs to derive mechanistic hypotheses.

Genomic positional conservation identifies topological anchor point (tap)RNAs linked to developmental loci

The paper identifies positionally conserved lncRNAs linked to developmental loci and chromatin organization, revealing their functional connections to coding genes and cancer metastasis.