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Complex genomic rearrangements (CGRs) consisting of two or more breakpoint junctions have been observed in genomic disorders. Recently, a chromosome catastrophe phenomenon termed chromothripsis, in which numerous genomic rearrangements are apparently acquired in one single catastrophic event, was described in multiple cancers. Here, we show that(More)
Duplication at the Xq28 band including the MECP2 gene is one of the most common genomic rearrangements identified in neurodevelopmentally delayed males. Such duplications are non-recurrent and can be generated by a non-homologous end joining (NHEJ) mechanism. We investigated the potential mechanisms for MECP2 duplication and examined whether genomic(More)
Currently most of state-of-the-art methods for Chinese word segmentation are based on supervised learning, whose features are mostly extracted from a local context. These methods cannot utilize the long distance information which is also crucial for word segmentation. In this paper, we propose a novel neural network model for Chinese word segmentation,(More)
The tasks in fine-grained opinion mining can be regarded as either a token-level sequence labeling problem or as a semantic compositional task. We propose a general class of discriminative models based on recurrent neural networks (RNNs) and word embeddings that can be successfully applied to such tasks without any task-specific feature engineering effort.(More)
The groundbreaking discovery of induced pluripotent stem cells (iPS cells) provides a new source for cell therapy. However, whether the iPS derived functional lineages from different cell origins have different immunogenicity remains unknown. It had been known that the cells isolated from extra-embryonic tissues, such as umbilical cord mesenchymal cells(More)
Word pairs, which are one of the most easily accessible features between two text segments, have been proven to be very useful for detecting the discourse relations held between text segments. However, because of the data sparsity problem, the performance achieved by using word pair features is limited. In this paper, in order to overcome the data sparsity(More)
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related(More)
  • THOMAS Y. HOUa, PENGFEI LIUb, +5 authors ω‖2H
  • 2016
We introduce a model reduction method for elliptic PDEs with random input, which follows the heterogeneous stochastic finite element method framework and exploits the compactness of the solution operator in the stochastic direction on local regions of the spatial domain. This method consists of two stages and suits the multi-query setting. In the offline(More)
We identified complex genomic rearrangements consisting of intermixed duplications and triplications of genomic segments at the MECP2 and PLP1 loci. These complex rearrangements were characterized by a triplicated segment embedded within a duplication in 11 unrelated subjects. Notably, only two breakpoint junctions were generated during each rearrangement(More)