Hongxu Ding

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts

This study shows that diverse training data improves basecalling accuracy for novel RNA modifications in nanopore sequencing.

An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control

This paper introduces an iterative method to improve nanopore sequencing basecalling accuracy for therapeutic RNA quality control, enabling precise analysis of modified RNAs.

The Precise Basecalling of Short-Read Nanopore Sequencing

This paper develops a basecalling scheme for short-read nanopore sequencing, showing improved accuracy using bioengineered RNA molecules.

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection

This paper adapts nanopore sequencing basecalling models for modification detection using incremental learning and anomaly detection, achieving single-molecule and single-nucleotide resolution across multiple species and modification types.

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

The paper identifies treatment-resistant Cancer Stem-Like Cells in breast cancer using single-cell RNA sequencing and validates a drug combination to target them effectively.

A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa

The paper develops a spatiotemporal multi-omics platform and machine-learning tool to manufacture hPSC-derived esophageal mucosa.

Airway Basal Cells Show Regionally Distinct Potential to Undergo Metaplastic Differentiation

The paper identifies six distinct basal cell subpopulations in the murine trachea, showing regional differences in their stemness and response to injury, with conservation in humans.

Concerted modification of nucleotides at functional centers of the ribosome revealed by single-molecule RNA modification profiling

The paper develops a method to profile single-molecule RNA modifications in yeast rRNA, revealing concerted modifications at functional centers and subpopulation changes in ribosome biogenesis.

Towards Inferring Nanopore Sequencing Ionic Currents from Nucleotide Chemical Structures

The paper presents a deep learning framework to predict nanopore sequencing ionic currents from nucleotide chemical structures and demonstrates its ability to generalize chemical information for modified nucleotides.

Patient Informed Domain Adaptation Improves Clinical Drug Response Prediction

This paper develops a deep learning framework to improve clinical drug response prediction by integrating gene expression and drug chemical structures, showing better performance than existing methods.

Biological network-inspired interpretable variational autoencoder

This paper introduces VEGA, a variational autoencoder with interpretable latent variables inspired by biological networks, demonstrating its ability to model cellular responses and regulatory mechanisms.

Biological Process Activity Transformation of Single Cell Gene Expression for Cross-Species Alignment

This paper transforms single-cell RNA-Seq data into biological process activities to enable cross-species alignment of cellular states.

Single-cell based elucidation of molecularly-distinct glioblastoma states and drug sensitivity

The paper identifies four molecularly distinct glioblastoma subtypes using single-cell protein-activity analysis and shows drug sensitivity is state-dependent.

Quantitative Assessment of Protein Activity in Orphan Tissues and Single Cells Using the metaVIPER Algorithm

The paper introduces metaVIPER, an algorithm to assess protein activity in orphan tissues and single cells by integrating multiple non-tissue-matched interactomes, improving reproducibility of protein-activity signatures.