A5038520945- Genomic variations and chromosomal abnormalities
- Genomics and Rare Diseases
- Genetics and Neurodevelopmental Disorders
- RNA Research and Splicing
- Cancer-related gene regulation
- Satellite Image Processing and Photogrammetry
- Epilepsy research and treatment
- CRISPR and Genetic Engineering
- Remote Sensing and Land Use
- RNA regulation and disease
- Congenital heart defects research
- Prenatal Screening and Diagnostics
- RNA and protein synthesis mechanisms
- Advanced Computational Techniques and Applications
Capital Institute of Pediatrics
2021-2023
Abstract Recurrent proximal 16p11.2 deletion (16p11.2del) is a risk factor for diverse neurodevelopmental disorders with incomplete penetrance and variable expressivity. Although investigation human induced pluripotent stem cell models has confirmed disruption of neuronal development in 16p11.2del cells, which genes are responsible abnormal cellular phenotypes what determines the abnormalities unknown. We performed haplotype phasing region cohort generated cells two families distinct...
Abstract Objective We determined the yield, genetic spectrum, and actual origin of de novo mutations (DNMs) for infantile spasms (ISs) in a Chinese cohort. The efficacy levetiracetam (LEV) STXBP1 ‐related ISs was explored also. Methods Targeted sequencing 153 epilepsy‐related candidate genes applied to 289 patients with undiagnosed ISs. Trio‐based amplicon deep used all DNMs distinguish somatic/mosaic from germline ones. Results Total 26 were identified recruited Among them, 24 interpreted...
ABSTRACT Recurrent proximal 16p11.2 deletion (16p11.2del) is risk factor of diverse neurodevelopmental disorders (NDDs) with variable penetrance. Although previous human induced pluripotent stem cell (hiPSC) models 16p11.2del confirmed disrupted neuron development, it not known which gene(s) at this interval are mainly responsible for the abnormal cellular phenotypes and how NDD penetrance regulated. After haplotype phasing region, we generated hiPSCs two families distinct residual...
Abstract We present a new approach for predicting functional sequence patterns in mRNA, known as motifs. These motifs play an important role understanding the mechanisms of cell life cycle clinical research and drug discovery. However, many existing neural network models mRNA event prediction only take input, do not consider positional information sequence. In contrast, motifNet is lightweight that uses both its input. This allows implicit representation various motif interaction human...