A5052753812- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- Smart Grid Energy Management
- Microgrid Control and Optimization
- Genomics and Phylogenetic Studies
- Genomics and Chromatin Dynamics
- Power Systems and Renewable Energy
- Electric Power System Optimization
- Health, Environment, Cognitive Aging
- Cancer Genomics and Diagnostics
- Congenital heart defects research
- Integrated Energy Systems Optimization
- Delphi Technique in Research
- Gut microbiota and health
- RNA and protein synthesis mechanisms
- RNA Research and Splicing
- Cancer-related molecular mechanisms research
- Immune responses and vaccinations
- Magneto-Optical Properties and Applications
- Genetics, Bioinformatics, and Biomedical Research
- Evolutionary Algorithms and Applications
- Frequency Control in Power Systems
- Energy Load and Power Forecasting
University of Cambridge
2022-2025
Cancer Research UK
2022-2025
Cancer Research UK Cambridge Center
2025
Tsinghua University
2016-2024
Southeast University
2022-2024
Center for Information Technology
2019-2022
Institut de Biologie systémique et synthétique
2021
Institute of Bioinformatics
2018-2019
Shanghai Center For Bioinformation Technology
2019
Multimedia University
2002
Abstract Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk mastectomies. From more than 800,000 cells identified 41 subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according natural history tissue. Age, parity germline mutations, known modulate developing cancer, affected homeostatic cellular state in ways. We found...
Glioblastoma is an incurable brain malignancy. By the time of clinical diagnosis, these tumours exhibit a degree genetic and cellular heterogeneity that provides few clues to mechanisms initiate drive gliomagenesis1,2. Here, explore early steps in gliomagenesis, we utilized conditional gene deletion lineage tracing tumour mouse models, coupled with serial magnetic resonance imaging, then closely track formation. We isolated labelled unlabelled cells at multiple stages—before first visible...
Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context tissue microenvironments. A fundamental task gene expression analysis is to identify genes with spatially variable patterns, or (SVgenes). Several computational methods have been developed this task. Their high complexity limited their scalability latest and future large-scale data.We present SOMDE, an efficient method identifying SVgenes data. SOMDE...
Summary: The Imaging and Molecular Annotation of Xenografts Tumors Cancer Grand Challenges team was set up with the objective developing “next generation” pathology cancer research by using a combination single-cell spatial omics tools to produce 3D molecularly annotated maps tumors. Its activities overlapped, in some cases catalyzed, revolution biology that saw new technologies being deployed investigate roles tumor heterogeneity micro-environment. See related article Stratton et al., p. 22...
The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases all cells human organs or the whole body. true assembly a cell atlas should be cell-centric rather than file-centric. We developed unified informatics framework seamless and built Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled 116 published datasets, covering 38 11 systems. invented three new methods applications based on assembly: "in data"...
<p>IMAXT Consortium Author List</p>
<div>Summary:<p>The Imaging and Molecular Annotation of Xenografts Tumors Cancer Grand Challenges team was set up with the objective developing “next generation” pathology cancer research by using a combination single-cell spatial omics tools to produce 3D molecularly annotated maps tumors. Its activities overlapped, in some cases catalyzed, revolution biology that saw new technologies being deployed investigate roles tumor heterogeneity micro-environment.</p><p><a...
<p>IMAXT Consortium Author List</p>
Abstract Data from clinical trials (CTs) drive advancements in practice. Despite most CTs now incorporating extensive translational portfolios, the diverse modalities of and sample data they generate often remain disconnected underutilised. SYNERGIA is a resource designed to integrate multi-modal multiple comparable format, that will be appropriately accessible clinicians researchers. The aims are to:1) Develop comprehensive, repository integrates longitudinal CT (&gt;5 years), with...
Abstract Summary Clustering is a key step in revealing heterogeneities single-cell data. Most existing clustering methods output fixed number of clusters without the hierarchical information. Classical (HC) provides dendrograms cells, but cannot scale to large datasets due high computational complexity. We present HGC, fast Hierarchical Graph-based tool address both problems. It combines advantages graph-based and HC. On shared nearest-neighbor graph HGC constructs tree with linear time...
Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, discovery combinatorial patterns from these CNN models has remained difficult. We show that main difficulty due problem multifaceted neurons which respond multiple types patterns. Since existing interpretation methods were mainly...
As large amounts of distributed renewable energy generation (DREG) replace conventional generating units on the grid, tension between supply lack flexible resources and increased demand for grid intensifies. To address this issue, paper focuses aggregation (DREGA) applications based storage systems (ESS). Considering interconnection supply–demand coupling consumption in ESS is used to flexibly regulate flow electrical producers consumers DREGA. A pricing methodology that takes into account...
Abstract Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements achieve interpretable annotations chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, deep learning framework that integrates sequence information...
This perspective discusses the need and directions for development of a unified information framework to enable assembly cell atlases revolution in medical research on virtual body assembled systems.
Single-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With exponential growth of number cells profiled individual scRNA-seq experiments, demand for identifying putative cell types from data has become great challenge that appeals novel computational methods. Although variety algorithms recently been proposed single-cell clustering, such limitations as low accuracy, inferior...
A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is vital importance for constructing large-scale cell atlases as references molecular cellular studies. Studies have shown exhibit multifaceted in transcriptomic features at multiple resolutions. This nature complexity makes it hard to design a fixed through combination known features. It desirable build learnable model major serve controlled generative data augmentation. We developed...
Abstract Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context tissue micro-environments. A fundamental task gene expression analysis is to identify genes with spatially variable patterns, or (SVgenes). Several computational methods have been developed this task. Their high complexity limited their scalability latest and future large-scale data. We present SOMDE, an efficient method identifying SVgenes...
Background Reproducibility is a defining feature of scientific discovery. can be at different levels for types study. The purpose the Human Cell Atlas (HCA) project to build maps molecular signatures all human cell and states serve as references future discoveries. Constructing such complex reference atlas must involve assembly aggregation data from multiple labs, probably generated with technologies. It has much higher requirements on reproducibility than individual research projects. To...
Metagenomic sequencing is a powerful technology for studying the mixture of microbes or microbiomes on human and in environment. One basic task analyzing metagenomic data to identify component genomes community. This challenging due complexity microbiome composition, limited availability known reference genomes, usually insufficient coverage.As an initial step toward understanding complete composition sample, we studied problem estimating total length all distinct sample. We showed that this...
Expectations of machine learning (ML) are high for discovering new patterns in high-throughput biological data, but most such practices accustomed to relying on existing knowledge conditions design experiments. Investigations the power and limitation ML revealing complex from data without guide have been lacking. In this study, we conducted systematic experiments ab initio discovery with methods single-cell RNA-sequencing early embryonic development. Results showed that a strategy combining...
Background Metagenomic sequencing is a complex sampling procedure from unknown mixtures of many genomes. Having metagenome data with known genome compositions essential for both benchmarking bioinformatics software and investigating influences various factors on the data. Compared to real microbiome samples or defined microbial mock community, simulated proper computational models are better purpose as they provide more flexibility controlling multiple factors. Methods We developed...
Abstract In order to solve the problem of foundation settlement monitoring, this paper presents a monitoring program substation settlement, which is based on distributed optical sensing technology. The composition hardware system and wiring scheme stress cables were described. results simulation experiment showed that Brillouin time domain analysis could well meet precision requirements monitoring. Moreover, temperature reference fibers should be laid for compensation. This can provide basis...