11810 results for "cancer rna-seq"

in 60.09 milliseconds.

• Identifying the role of ID3 in DNA repair and maintenance of genome integrity (ATAC-seq)

  Dataset EGAD00001008199

• EAC-protein

  This dataset contains single cell protein tag sequencing of EAC samples by 10x Genomics CITE-seq. Total number of files - 24. File format - FASTQ.

  Dataset EGAD50000000751

• Single-cell G&T sequencing - Transcriptomic data

  Single-cell G&T seq from an untreated PDX mouse with neuroblastoma (MYCN amplified).

  Dataset EGAD50000001514

• Low coverage whole genome sequencing form CSF-derived cell free DNA

  Low coverage whole genome sequencing form CSF-derived cell free DNA for matching samples with EM-seq cohort

  Dataset EGAD50000002148

• Histone modifications of cfDNA

  59 samples are sequenced, 18 are HCC and beta thalassemia cases while the remaining are control case.

  Dataset EGAD00001009267

• SF10679

  SF10679 snATAC Seq. Oligodendroglioma, Anaplastic (WHO gr. 3). Tumor Location: Frontal Age: 43. Sex: Male.

  Dataset EGAD00001006312

• Nascent transcriptome in T-ALL

  Nascent transcriptome (GRO-seq) data representing bone marrow mononuclear cells of two diagnostic T-ALL samples.

  Dataset EGAD00001008410

• DLBCL Validation (FFPE) Cohort

  miRNA-seq Cohort of 140 Formalin Fixed Paraffin Embedded Diffuse Large B-cell Lymphoma Patient Samples

  Dataset EGAD00001001073

• SF4007

  SF4007 snATAC Seq. Oligodendroglioma (WHO gr. 2) Tumor Location: Left frontotemporal. Age:33. Sex: Female.

  Dataset EGAD00001006317

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861