BACKGROUND: Glioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urge...
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
SLAS discovery : advancing life sciences R & D
28231035
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medic...
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
32098890
BACKGROUND: Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel ...
Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here,...
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tum...
Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populati...
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...