A5087981982- Single-cell and spatial transcriptomics
- Extracellular vesicles in disease
- Gene expression and cancer classification
- Cancer-related molecular mechanisms research
- Birth, Development, and Health
- Bioinformatics and Genomic Networks
- Computational Drug Discovery Methods
- Gut microbiota and health
- Neuroinflammation and Neurodegeneration Mechanisms
- Diabetes and associated disorders
- Immune cells in cancer
- Cancer Immunotherapy and Biomarkers
- Network Security and Intrusion Detection
- Immune Cell Function and Interaction
- Pancreatic function and diabetes
- Alzheimer's disease research and treatments
- Cell Image Analysis Techniques
- Microbial Metabolic Engineering and Bioproduction
- Pluripotent Stem Cells Research
- Spam and Phishing Detection
- Systemic Lupus Erythematosus Research
- Neuroendocrine regulation and behavior
- Gene Regulatory Network Analysis
- Advanced Malware Detection Techniques
- CAR-T cell therapy research
Genome Institute of Singapore
2020-2024
Agency for Science, Technology and Research
2020-2024
European Molecular Biology Laboratory
2024
Global Inkjet Systems (United Kingdom)
2020
Nanyang Technological University
2018
Abstract The gut microbiota operates at the interface of host–environment interactions to influence human homoeostasis and metabolic networks 1–4 . Environmental factors that unbalance microbial ecosystems can therefore shape physiological disease-associated responses across somatic tissues 5–9 However, systemic impact microbiome on germline—and consequently F 1 offspring it gives rise to—is unexplored 10 Here we show act as a key between paternal preconception environment intergenerational...
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and thus a key component of single cell pipelines. Existing feature methods perform inconsistently across datasets, occasionally even resulting in poorer accuracy than without selection. Moreover, existing ignore information contained gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), algorithm that leverages correlations with novel...
Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting
Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use reference panel labelled transcriptomes guide both clustering and type identification. Supervised approaches have their distinct advantages limitations. Therefore, they can lead different but often complementary results. Hence, consensus approach...
Abstract The transcriptomic diversity of cell types in the human body can be analysed unprecedented detail using single (SC) technologies. Unsupervised clustering SC transcriptomes, which is default technique for defining types, prone to group cells by technical, rather than biological, variation. Compared de-novo (unsupervised) clustering, we demonstrate multiple benchmarks that supervised uses reference transcriptomes as a guide, robust batch effects and data quality artifacts. Here,...
Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD characterized by an increase in amyloid metabolism, misfolding deposition β-amyloid oligomers around neurons brain. These processes remodel calcium signaling mechanism neurons, leading to cell death via apoptosis. Despite accumulating knowledge about biological underlying AD, mathematical models date are restricted depicting only small...
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and thus a key component of single cell pipelines. However, we found that the performance existing feature methods was inconsistent across benchmark datasets, occasionally even worse than without selection. Moreover, ignored information contained in gene-gene correlations. We therefore developed DUBStepR ( D etermining U nderlying B asis using Step wise R egression), algorithm leverages correlations...
The paternal preconception environment has been implicated as a modulator of phenotypic traits and disease risk in F1 offspring. However, the prevalence mechanisms such intergenerational epigenetic inheritance (IEI) mammals remain poorly defined. Moreover, interplay between exposure, genetics, age on emergent offspring features is unexplored. Here, we measure quantitative impact three environments early embryogenesis across genetic backgrounds. Using vitro fertilisation (IVF) at scale,...
Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use reference panel labelled transcriptomes guide both and type identification. Supervised strategies have their distinct advantages limitations. Therefore, they can lead different but often complementary results. Hence, consensus approach leveraging merits paradigms...
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and thus a key component of single cell pipelines. However, we found that the performance existing feature methods was inconsistent across benchmark datasets, occasionally even worse than without selection. Moreover, ignored information contained in gene-gene correlations. We therefore developed DUBStepR (Determining Underlying Basis using Stepwise Regression), algorithm leverages correlations with...
Motivation The transcriptomic diversity of the hundreds cell types in human body can be analysed unprecedented detail using single (SC) technologies. Though clustering cellular transcriptomes is default technique for defining and subtypes, strongly influenced by technical variation. In fact, prevalent unsupervised algorithms cluster cells technical, rather than biological, Results Compared to de novo (unsupervised) methods, we demonstrate multiple benchmarks that supervised clustering, which...
The online social networks slowly incorporate economical competencies toward empowering the use of virtual and real currency. They provide as novel platforms to host an assortment for business exercises, where clients might potentially get cash rewards by taking interest such occasions. Both OSNs accomplices are fundamentally worried when attackers instrument a group accounts gather money from these events that settle on occasions Insuffcient result in important fnancial loss. It gets be...