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Multi-Source Neural Model for Machine Translation of Agglutinative Language
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In: Future Internet ; Volume 12 ; Issue 6 (2020)
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| 2 |
A High Efficient Biological Language Model for Predicting Protein–Protein Interactions
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In: Cells ; Volume 8 ; Issue 2 (2019)
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Abstract:
Many life activities and key functions in organisms are maintained by different types of protein&ndash ; protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein&ndash ; protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a &ldquo ; bio-word&rdquo ; segmentation system and a word representation model used for learning the distributed representation for each &ldquo ; bio-word&rdquo ; . The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of &ldquo ; bio language processing technology,&rdquo ; which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.
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Keyword:
bio-language processing; convolution neural network; protein–protein interactions; sentencepiece; unigram language model
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URL: https://doi.org/10.3390/cells8020122
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| 3 |
Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages
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In: Information ; Volume 11 ; Issue 1 (2019)
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| 4 |
Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection
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In: Applied Sciences ; Volume 9 ; Issue 16 (2019)
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| 5 |
The ingredients of comparison: The semantics of the excessive construction in Japanese
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In: Semantics and Pragmatics, Vol 8, Iss 0, Pp 1-38 (2015) (2015)
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| 6 |
Machine Learning Paradigms for Speech Recognition: An Overview
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| 7 |
Sequence Clustering and Labeling for Unsupervised Query Intent Discovery
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In: http://www.cs.utoronto.ca/%7Ejcheung/papers/wsdm2012.pdf (2012)
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| 8 |
Understanding the semantic structure of noun phrase queries
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In: http://research.microsoft.com/pubs/130815/acl.pdf (2010)
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| 9 |
Semi-supervised learning of semantic classes for query . . .
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In: http://research.microsoft.com/pubs/101154/fp0894-wang-webpost.pdf (2009)
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| 10 |
Rejoinder: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
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| 11 |
The Vocal Joystick data collection effort and vowel corpus
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In: http://ssli.ee.washington.edu/people/bilmes/mypapers/VJ_ICSLP_2006_v8.pdf (2006)
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| 12 |
The Vocal Joystick: A voice-based humancomputer interface for individuals with motor impairments
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In: https://www.ee.washington.edu/techsite/papers/documents/UWEETR-2005-0007.pdf (2005)
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| 13 |
The Vocal Joystick Demo at UIST05: A Voice-Based Human-Computer Interface
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In: http://ssli.ee.washington.edu/vj/files/UIST-demo-abstract.pdf (2005)
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| 14 |
Algorithms for data-driven ASR parameter quantization
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In: http://ssli.ee.washington.edu/people/bilmes/mypapers/quan-algo-csl-sdarticle.pdf (2005)
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| 15 |
The Vocal Joystick: A Voice-Based Human-Computer Interface for Individuals with Motor Impairments
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In: http://ssli.ee.washington.edu/people/bilmes/mypapers/uist05.pdf (2005)
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| 16 |
Algorithms for Data-Driven ASR Parameter Quantization
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In: http://ssli.ee.washington.edu/people/karim/papers/quan-algorithms.pdf (2003)
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| 17 |
A Phrase Table Filtering Model Based on Binary Classification for Uyghur-Chinese Machine Translation
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In: http://www.jcomputers.us/vol9/jcp0912-02.pdf
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| 18 |
General Terms
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In: http://dub.washington.edu/pubs/assets2006/assets71-harada.pdf
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| 19 |
LEXICON MODELING FOR QUERY UNDERSTANDING
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In: http://groups.csail.mit.edu/sls/publications/2011/Liu_ICASSP2011.pdf
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| 20 |
Understanding the semantic structure of noun phrase queries. ACL’10
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In: http://aclweb.org/anthology-new/P/P10/P10-1136.pdf
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