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Smart Auto-completion in Live Chat Utilizing the Power of T5 ; Smart automatisk komplettering i livechatt som utnyttjar styrkan hos T5
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Wang, Zhanpeng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Sequence-to-Sequence Acoustic Modeling with Semi-Stepwise Monotonic Attention for Speech Synthesis
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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Improving Grapheme-to-Phoneme Conversion for Anglicisms in German Speech Recognition
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In: Fraunhofer IAIS (2021)
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Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders
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Devanna, P; Chen, X S; Ho, J; Gajewski, D; Smith, S D; Gialluisi, A; Francks, C; Fisher, S E; Newbury, D F; Vernes, S C. - 2021
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Abstract:
Funding: This work was funded by a Marie Curie Career Integration Grant and by a Max Planck Research Group Grant both awarded to SCV. The work of the Newbury lab is funded by the Medical Research Council (G1000569/1 and MR/J003719/1). XSC, AG, CF and SEF were supported by the Max Planck Society. The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provided core support for ALSPAC. The work of the Wellcome Trust Centre in Oxford is supported by the Wellcome Trust (090532/Z/09/Z). JH was supported by a scholarship from the Agency for Science, Technology, and Research, Singapore. The work of SDS is supported by the grant HD027802 from NIH. ; Understanding the genetic factors underlying neurodevelopmental and neuropsychiatric disorders is a major challenge given their prevalence and potential severity for quality of life. While large-scale genomic screens have made major advances in this area, for many disorders the genetic underpinnings are complex and poorly understood. To date the field has focused predominantly on protein coding variation, but given the importance of tightly controlled gene expression for normal brain development and disorder, variation that affects non-coding regulatory regions of the genome is likely to play an important role in these phenotypes. Herein we show the importance of 3 prime untranslated region (3'UTR) non-coding regulatory variants across neurodevelopmental and neuropsychiatric disorders. We devised a pipeline for identifying and functionally validating putatively pathogenic variants from next generation sequencing (NGS) data. We applied this pipeline to a cohort of children with severe specific language impairment (SLI) and identified a functional, SLI-associated variant affecting gene regulation in cells and post-mortem human brain. This variant and the affected gene (ARHGEF39) represent new putative risk factors for SLI. Furthermore, we identified 3'UTR regulatory variants across autism, schizophrenia and bipolar disorder NGS cohorts demonstrating their impact on neurodevelopmental and neuropsychiatric disorders. Our findings show the importance of investigating non-coding regulatory variants when determining risk factors contributing to neurodevelopmental and neuropsychiatric disorders. In the future, integration of such regulatory variation with protein coding changes will be essential for uncovering the genetic causes of complex neurological disorders and the fundamental mechanisms underlying health and disease. ; Publisher PDF ; Peer reviewed
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Keyword:
3' Untranslated Regions/genetics; Adult; Autistic disorder/genetics; Binding sites/genetics; Bipolar disorder/genetics; Child; Cohort studies; DAS; DNA; Female; Gene expression regulation/genetics; Genetic predisposition to Disease; Genetic variation/genetics; Genomics; High-throughput nucleotide sequencing/methods; Humans; intergenic/genetics; Language development disorders/genetics; Male; Mental disorders/genetics; microRNAs/genetics; Nervous system diseases/genetics; Neurodevelopmental disorders/genetics; QH301; QH301 Biology; QH426; QH426 Genetics; RC0321; RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry; Schizophrenia/genetics; Sequence analysis/methods
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URL: https://doi.org/10.1038/mp.2017.30
http://hdl.handle.net/10023/21699
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| 5 |
Asm2Seq: Explainable Assembly Code Functional Summary Generation
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The Rare Word Issue in Natural Language Generation: A Character-Based Solution
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Controllable Sentence Simplification
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678214 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
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| 8 |
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
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In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
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| 9 |
Tamil Paraphrase Detection Using Encoder-Decoder Neural Networks
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In: IFIP Advances in Information and Communication Technology ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS) ; https://hal.inria.fr/hal-03434784 ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.30-42, ⟨10.1007/978-3-030-63467-4_3⟩ (2020)
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A Data-Efficient End-to-End Spoken Language Understanding Architecture
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In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP) ; https://hal.archives-ouvertes.fr/hal-03094850 ; International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020, Barcellone, Spain (2020)
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Comparison of Word2vec with Hash2vec for Machine Translation
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In: Master's Projects (2020)
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Advances in deep learning methods for speech recognition and understanding
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Interpreting Sequence-to-Sequence Models for Russian Inflectional Morphology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
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In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
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SEQUENCE-TO-SEQUENCE MODELLING OF F0 FOR SPEECH EMOTION CONVERSION
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In: IEEE International Conference on Acoustics, Speech, and Signal Processing ; https://hal.sorbonne-universite.fr/hal-02018439 ; IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2019, Brighton, United Kingdom (2019)
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| 16 |
Abstractive Sentence Compression with Event Attention
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In: Applied Sciences ; Volume 9 ; Issue 19 (2019)
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Diverse Decoding for Abstractive Document Summarization
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In: Applied Sciences ; Volume 9 ; Issue 3 (2019)
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| 18 |
Neural Sign Language Translation Based on Human Keypoint Estimation
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In: Applied Sciences ; Volume 9 ; Issue 13 (2019)
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Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding
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