Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Deep Learning-based Pathway-Centric approach to characterize Recurrent Hepatocellular Carcinoma after Liver Transplantation

ABSTRACT: Deep Learning-based Pathway-Centric approach to characterize Recurrent Hepatocellular Carcinoma after Liver Transplantation

PROVIDER: PRJNA1051125 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Deep Learning-based Pathway-Centric approach to characterize Recurrent Hepatocellular Carcinoma after Liver Transplantation

Project description:This study analyzed the clonal evolution at the transcriptomic level during the recurrence of hepatocellular carcinoma (HCC) after liver transplantation, utilizing a rare collection of primary and recurrent tumors.

2024-06-06 | GSE249913 | GEO

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

Project description: Not available

| S-EPMC11151487 | biostudies-literature

Recurrent disruption of HIP1 identified in patients with epilepsy, learning difficulties, intellectual disability, and neurobehavioral abnormalities

Project description:We report 12 individuals from ten unrelated families with epilepsy, learning difficulties, intellectual disability, and neurobehavioral abnormalities, who segregated a microdeletion distally adjacent to the Williams-Beuren syndrome region. In six families, a recurrent ~ 1.1 Mb deletion likely resulted from nonallelic homologous recombination between flanking large complex low-copy repeats. Three smaller sized microdeletions (~ 180-500 kb) enabled us to narrow the critical region to one gene, HIP1, encoding Huntington interacting protein 1. genomic DNA from blood was used to analyse 12 samples with control sample using custom made NimbleGen 12x135K microarrays for chr7 region.

2010-08-28 | E-GEOD-23834 | biostudies-arrayexpress

Recurrent disruption of HIP1 identified in patients with epilepsy, learning difficulties, intellectual disability, and neurobehavioral abnormalities

2010-08-28 | GSE23834 | GEO

learning

Project description:learning

| PRJEB26278 | ENA

Blood-Based Gene Signatures Associated with Therapeutic Response to Anti-TNF Therapy in Rheumatoid Arthritis: A Combined Meta-Analytical and Machine Learning Approach

Project description:Objective Tumor necrosis factor alpha (TNF-α) inhibitors have significantly improved outcomes in rheumatoid arthritis (RA); however, up to 30–40% of patients show inadequate response. This highlights the need for predictive biomarkers to guide personalized therapy. Blood transcriptome profiling is a promising strategy for identifying gene signatures linked to treatment response, though prior studies have shown inconsistent results due to technical and biological heterogeneity. Methods We performed a comprehensive meta-analysis of baseline blood transcriptome datasets from eight independent RA cohorts to identify genes consistently upregulated in patients who responded to TNF-α inhibitors. This analysis identified a core set of 39 recurrent genes (the Recurrent gene set). To evaluate its predictive relevance, we trained machine learning models using four gene subsets: the Recurrent set, the Patent gene set derived from a biomarker patent, the Top-ranked gene set based on Cohen’s d effect size, and the full gene set. External validation was performed using an independent dataset. Results The Recurrent gene set showed strong and consistent predictive performance across machine learning models. Notably, several genes overlapping with the Patent and Top-ranked sets were among the top contributors, supporting their potential value for future diagnostic applications. Conclusion Our study demonstrates the feasibility of transcriptome-guided prediction of response to TNF-α inhibitors in RA. The integration of meta-analysis and machine learning provides a strong foundation for the development of precision diagnostic tools to support personalized treatment strategies in clinical practice.

2026-07-01 | GSE309430 | GEO

TMPRSS2-ERG, HDACs and EZH2 are involved in an AR-centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression

Project description:This SuperSeries is composed of the following subset Series: GSE28948: TMPRSS2-ERG, HDACs and EZH2 are involved in an AR-centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression (gene expression data) GSE28950: TMPRSS2-ERG, HDACs and EZH2 are involved in an AR-centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression (ChIP-Seq data) GSE35540: TMPRSS2-ERG, HDACs and EZH2 are involved in an AR centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression (gene expression after ERG KD) Refer to individual Series

2012-04-26 | E-GEOD-28951 | biostudies-arrayexpress

A Deep Learning Approach For Programmable RNA Switches

Project description:Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these tools remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Thus, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesized and characterized in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperformed (R2=0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2=0.04-0.15) and allowed for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This deep learning approach constitutes a major step forward in engineering and understanding of RNA synthetic biology.

2020-08-12 | GSE149225 | GEO

A Deep Learning Approach For Programmable RNA Switches

Project description:A Deep Learning Approach For Programmable RNA Switches

| PRJNA627694 | ENA