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Articles by Xiaochen Bo
Recommendation system-inspired learning of crossmodal interaction patterns between chromatin accessibility and gene expression
Abstract Despite the growing availability of crossmodal single-cell sequencing, existing approaches are limited by underutilization of biological attributes, sparse modality signals, and insufficient modeling of regulatory interactions, limiting generalization in data-scarce settings and the discovery of peak-gene-level relationships.
A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations
Abstract Small proteins, with more pronounced conformational flexibility, are crucial in biological processes and their function is linked to their high flexibility. While several databases are widely used to characterize protein dynamics, a significant data gap remains for small proteins comprising 5–100 amino acids. To address this, we present DynoDB, a dataset of small protein conformational ensembles from all-atom molecular dynamics simulations.
ToxiSpecies: Task-Aware Meta-Learning for Cross-Species Modeling of Acute Chemical Toxicity under Distribution Shift Click to copy article link Article link copied!
Abstract Click to copy section linkSection link copied! Reliable modeling of chemical toxicity across species poses a fundamental challenge for molecular property prediction, as toxicity data sets are inherently heterogeneous in species coverage, endpoints, measurement scales, and data availability.
Generative pretraining for drug molecule design with bidirectional structure-property optimization
Abstract Designing drug-like molecules that satisfy both properties requirements and structural constraints remains challenging. Current generative approaches typically reduce properties to numerical constraints and treat molecular structures as deterministic functions of properties, failing to capture complex, nonlinear structure-property relationships and limiting generative diversity and controllability.
Genome-guided generative adversarial learning enables nanopore adaptive sequencing
Abstract Nanopore adaptive sequencing enables real-time target enrichment, yet current deep-learning methods require costly, sample-specific experimental training data. To address this, we developed GANBase, a genome-guided generative adversarial learning framework, which is trained exclusively on reference sequences and incorporates a Monte Carlo Tree Search-based Rollout strategy for model training.
Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks
Abstract Sequence-based deep learning has advanced genome interpretation, yet most models remain task-specific and rely on retraining, limiting scalability across biological contexts. Here we present SUCCEED, a supervised multi-task DNA foundation model pretrained on 6,389 ENCODE functional genomics tracks to learn transferable regulatory representations.
A meta learning and task adaptive approach for drug target affinity prediction
Abstract Accurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction, existing methods struggle with limited training data and poor generalization. In this study, we propose AdaMBind, a novel DTA prediction model based on meta-learning framework with an adaptive task module designed for low-data scenarios.
Preclinical evaluation of AGT mRNA replacement therapy for primary hyperoxaluria type I disease
SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction
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3D genome landscape of primary and metastatic colorectal carcinoma reveals the regulatory mechanism of tumorigenic and metastatic gene expression - Communications Biology
Abstract Colorectal carcinoma (CRC) is a deadly cancer with an aggressive nature, and how CRC tumor cells manage to translocate and proliferate in a new tissue environment remains not fully understood.
Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
DANet: A Domain Alignment Network for Low-Light Image Enhancement
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains - Nature Communications
Abstract Topologically associating domains (TADs), megabase-scale features of chromatin spatial architecture, are organized in a domain-within-domain TAD hierarchy. Within TADs, the inner and smaller subTADs not only manifest cell-to-cell variability, but also precisely regulate transcription and differentiation. Although over 20 TAD callers are able to detect TAD, their usability in biomedicine is confined by a disagreement of outputs and a limit in understanding TAD hierarchy.
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS - Nature Biotechnology
Abstract Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data.
Comparative study on genomic and epigenomic profiles of retinoblastoma or tuberous sclerosis complex via nanopore sequencing and a joint screening framework | Cancer Gene Therapy
Abstract Recurrence and extraocular metastasis in advanced intraocular retinoblastoma (RB) are still major obstacles for successful treatment of Chinese children. Tuberous sclerosis complex (TSC) is a very rare, multisystemic genetic disorder characterized by hamartomatous growth. In this study, we aimed to compare genomic and epigenomic profiles with human RB or TSC using recently developed nanopore sequencing, and to identify disease-associated variations or genes.
Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing - Nature Communications
Abstract Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation.
EnrichMiner: a biologist-oriented web server for mining biological insights from functional enrichment analysis results
Abstract Functional enrichment analysis has been widely used to help researchers obtain biological insights from -omics data. However, the results are often redundant and difficult to digest. The key is developing tools to help users explore the relationships between the enriched terms, remove the redundant terms, and finally select representative terms.
PC3T: a signature-driven predictor of chemical compounds for cellular transition - Communications Biology
Abstract Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues.
TimeTalk uses single-cell RNA-seq datasets to decipher cell-cell communication during early embryo development - Communications Biology
Abstract Early embryonic development is a dynamic process that relies on proper cell-cell communication to form a correctly patterned embryo. Early embryo development-related ligand-receptor pairs (eLRs) have been shown to guide cell fate decisions and morphogenesis. However, the scope of eLRs and their influence on early embryo development remain elusive.
A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures
↵* Corresponding author; email: chenhb{at}bmi.ac.cn Abstract Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and in how to interpret the biological significance of the traits used for estimation.
EnrichMiner: a biologist-oriented web server for mining biological insights from functional enrichment analysis
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A benchmark study of deep learning-based multi-omics data fusion methods for cancer - Genome Biology
Presentation of the ten evaluation models This study considered p omics matrices Xi (i = 1, …, p) with a dimension of m × ni (m samples and ni features). Each sample can be represented by p vectors xi (i = 1, …, p). Note that the original models of all methods in this part can be found in the publications [19,20,21,22, 24, 26, 28, 29, 32,33,34, 38, 39]. FCNN FCNN is a simulated neural network and usually consists of an input layer, multiple hidden layers, and an output layer.
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