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We show that vlincRNAs genes likely function in cis to activate nearby genes
Present a valuable resource for drug discovery and have identified ROM as a promising therapeutic for MLL-rearranged iALL
VEGFR-3 signaling plays a central role in lymphatic biology, both in the development of the lymphatic network during embryogenesis as well as in...
The analysis of CAGE (Cap Analysis of Gene Expression) time-courses has been applied to examine the dynamics of enhancer and promoter by sequentially...
A comprehensive picture of the regulatory regions of the three genes involved in Rett Syndrome
Computer vision technology is advancing rare disease diagnosis to address unmet needs of the more than 300 million individuals affected globally; one in three rare diseases have a known facial phenotype. 3D face model reconstruction is a key driver of these advances.
People living with rare diseases (PLWRD) still face huge unmet needs, in part due to the fact that care systems are not sufficiently aligned with their needs and healthcare workforce (HWF) along their care pathways lacks competencies to efficiently tackle rare disease-specific challenges. Level of rare disease knowledge and awareness among the current and future HWF is insufficient.
In clinical genetics, establishing an accurate nosology requires analysis of variations in both aetiology and the resulting phenotypes. At the phenotypic level, recognising typical facial gestalts has long supported clinical and molecular diagnosis; however, the objective analysis of facial phenotypic variation remains underdeveloped.
The immunological changes underpinning acquisition of remission (also called sustained unresponsiveness) following food immunotherapy remain poorly defined. Limited access to effective therapies and biosamples from treatment responders has prevented progress. Probiotic peanut oral immunotherapy is highly effective at inducing remission, providing an opportunity to investigate immune changes.
To define clinical common data elements (CDEs) and a mandatory minimum data set (MDS) for genomic studies of cerebral palsy (CP). Method: Candidate data elements were collated following a review of the literature and existing CDEs.