A5100393711- Single-cell and spatial transcriptomics
- Genomics and Chromatin Dynamics
- Bioinformatics and Genomic Networks
- Epigenetics and DNA Methylation
- Computational Drug Discovery Methods
- Cell Image Analysis Techniques
- Gene expression and cancer classification
- RNA modifications and cancer
- Neural Networks and Applications
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- AI in cancer detection
- Protein Degradation and Inhibitors
- Topic Modeling
- Protein Structure and Dynamics
- Cancer Genomics and Diagnostics
- Gene Regulatory Network Analysis
- RNA Research and Splicing
- CRISPR and Genetic Engineering
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Histone Deacetylase Inhibitors Research
- Gut microbiota and health
- Cancer, Hypoxia, and Metabolism
Stanford University
2019-2025
Jiangsu Normal University
2023-2024
State Key Laboratory of Reproductive Medicine
2024
Shandong University
2011-2024
Second Xiangya Hospital of Central South University
2022-2024
Central South University
2022-2024
Guangdong Pharmaceutical University
2024
BioTheryX (United States)
2024
West China Second University Hospital of Sichuan University
2024
Sichuan University
2024
Abstract Motivation Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty efficacy and heterogeneity patients. Strong evidences have implicated high dependence CDR on tumor genomic transcriptomic profiles individual Precise identification crucial in both guiding anti-cancer design understanding biology. Results In this study, we present DeepCDR which integrates multi-omics cells explores intrinsic chemical structures drugs for predicting CDR. Specifically, a...
Abstract Motivation Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution data directly impacts the effectiveness and accuracy downstream analysis such as identifying topologically associating domains (TADs) meaningful loops. High are valuable resources which implicate relationship genome function, especially linking distal regulatory elements to their target genes. However, high across...
Significance Density estimation is among the most fundamental problems in statistics. It notoriously difficult to estimate density of high-dimensional data due “curse dimensionality.” Here, we introduce a new general-purpose estimator based on deep generative neural networks. By modeling normally distributed around manifold reduced dimension, show how power bidirectional networks (e.g., cycleGAN) can be exploited for explicit evaluation density. Simulation and real experiments suggest that...
Abstract Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets cellular state, producing datasets with millions observations. While these data hold great promise for understanding molecular mechanisms health and disease, analysis challenges arising from sparsity, technical biological variability, high dimensionality hinder derivation such mechanistic insights. To promote innovation algorithms...
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), graph completion (FKGC) has recently gained more research interests. Some existing models employ a relation's multi-hop neighbor information to enhance its semantic representation. However, noise might be amplified when the neighborhood is excessively sparse and no available represent relation. Moreover, modeling inferring complex relations of one-to-many (1-N), many-to-one (N-1), many-to-many (N-N) by previous...
Abstract Accurate and robust prediction of patient-specific responses to a new compound is critical personalized drug discovery development. However, patient data are often too scarce train generalized machine learning model. Although many methods have been developed utilize cell-line screens for predicting clinical responses, their performances unreliable owing heterogeneity distribution shift. Here we novel context-aware deconfounding autoencoder (CODE-AE) that can extract intrinsic...
Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin cfDNA can reveal abnormal because diseases, which has great clinical potential in disease detection and monitoring. Despite promise, sensitive accurate quantification tissue-derived remains challenging to existing methods due limited characterization methylation reliance on unsupervised methods. To fully exploit cfDNA, here we present one largest comprehensive high-resolution...
Abstract In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based which demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance available deconvolution in proportion analysis. this study, we introduce Deconvolution Evaluator (Deconer), toolkit the methods. Deconer provides various simulated real gene expression datasets, including...
A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards annotation human genome, still remains big challenge accurately annotate regulatory elements...
Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological processes chromosome packaging, transcriptional activation, and DNA damage. However, high-throughput experimental techniques ChIP-seq are usually expensive time-consuming, prohibiting establishment modification landscape hundreds cell types across dozens markers. These disadvantages have been appealing computational methods to complement approaches towards...
CRISPR-Cas is a powerful genome editing technology and has great potential for in vivo gene therapy. Successful translational application of to biomedicine still faces many safety concerns, including off-target side effect, cell fitness problem after treatment, on-target effect undesired tissues. To solve these issues, it needed design sgRNA with high cell-specific efficacy specificity. Existing single-guide RNA (sgRNA) tools mainly depend on sequence the local information targeted genome,...
Unsupervised domain adaptation (UDA) has been received increasing attention since it does not require labels in target domain. Most existing UDA methods learn domain-invariant features by minimizing discrepancy distance computed a certain metric between domains. However, these discrepancy-based cannot be robustly applied to unsupervised time series (UTSDA). That is because metrics contain only low-order and local statistics, which have limited expression for distributions therefore result...
Abstract Technological development has enabled the profiling of gene expression and chromatin accessibility from same cell. We develop scREG, a dimension reduction methodology, based on concept cis -regulatory potential, for single cell multiome data. This is further used construction subpopulation-specific networks. The capability inferring useful regulatory network demonstrated by two-fold increment inference accuracy compared to Pearson correlation-based method 27-fold enrichment GWAS...
Epigenetic dysregulation is reported in multiple cancers including Ewing sarcoma (EwS). However, the epigenetic networks underlying maintenance of oncogenic signaling and therapeutic response remain unclear. Using a series epigenetics- complex-focused CRISPR screens, RUVBL1, ATPase component NuA4 histone acetyltransferase complex, identified to be essential for EwS tumor progression. Suppression RUVBL1 leads attenuated growth, loss H4 acetylation, ablated MYC signaling. Mechanistically,...
Computational methods for DDIs and DTIs prediction are essential accelerating the drug discovery process. We proposed a novel deep learning method DeepDrug, to tackle these two problems within unified framework. DeepDrug is capable of extracting comprehensive features both target protein, thus demonstrating superior performance in series experiments. The downstream applications show that useful facilitating repositioning discovering potential against specific disease. Background approaches...
Battery management system (BMS) is an integral part of automobile. It protects the battery from damage, predicts life and maintains in operational condition. The BMS performs these tasks by integrating one or more functions, such as protecting cell, controlling charge, determining state charge (SOC), health (SOH), remaining useful (RUL) battery, cell balancing, well monitoring storing historical data. In this paper, we propose a that estimates three critical characteristics (SOC, SOH, RUL)...
Abstract Identification of novel functional domains and characterization detailed regulatory mechanisms in cancer-driving genes is critical for advanced cancer therapy. To date, CRISPR gene editing has primarily been applied to defining the role individual genes. Recently, high-density mutagenesis via tiling gene-coding exons demonstrated identify regions Furthermore, breakthroughs combining library screens with single-cell droplet RNA sequencing (sc-RNAseq) platforms have revealed capacity...
Drug resistance is a critical obstacle in cancer therapy. Discovering drug response important to improve anti-cancer treatment and guide design. Abundant genomic resources of cell lines provide unprecedented opportunities for such study. However, cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from vitro single-cell clinical data will be promising direction better understand resistance. Most current studies include single nucleotide variants (SNV)...
Summary Mammalian cells may undergo permanent growth arrest/senescence when they incur excessive DNA damage. As a key player during damage response ( DDR ), p53 transactivates an array of target genes that are involved in various cellular processes including the induction senescence. Chemokine receptor CXCR 2 was previously reported to mediate replicative and oncogene‐induced senescence p53‐dependent manner. Here, we report is upregulated types genotoxic or oxidative stress. Unexpectedly,...