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Ngôn ngữ:
Giới hạn tìm kiếm: Giới hạn tìm kiếm: Dạng tài nguyên Hiển thị kết quả với: Hiển thị kết quả với: Chỉ mục

A hybrid approach to finding phenotype candidates in genetic text = m���t h�����ng ti���p c���n lai ����� nh���n d���ng c��c ���ng vi��n ki���u h��nh trong d��� li���u sinh h���c. Lu���n v��n ThS. C��ng ngh��� th��ng tin: 60 48 01

L�� Ho��ng Qu���nh; H�� Quang Th���y ng�����i h�����ng d���n

H. : ��HCN, 2012 - (005.7 LE-Q 2012)

Truy cập trực tuyến

  • Nhan đề:
    A hybrid approach to finding phenotype candidates in genetic text = m���t h�����ng ti���p c���n lai ����� nh���n d���ng c��c ���ng vi��n ki���u h��nh trong d��� li���u sinh h���c. Lu���n v��n ThS. C��ng ngh��� th��ng tin: 60 48 01
  • Tác giả: L�� Ho��ng Qu���nh
  • H�� Quang Th���y ng�����i h�����ng d���n
  • Chủ đề: C��ng ngh��� th��ng tin; Khoa h���c m��y t��nh; D��� li���u sinh h���c
  • Mô tả: Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phe-notype terms are expected to play a key role in inferring gene function in complex heritable diseases but are intrinsically difficult to analyse due to their complex se-mantics and scale. In contrast to previous approaches we evaluate state-of-the-art techniques involving the fusion of machine learning on a rich feature set with evi-dence from extant domain knowledge-sources. The techniques are validated on two gold standard collections including a novel annotated collection of 112 abstracts de-rived from a systematic search of the Online Mendelian Inheritance of Man database for auto-immune diseases. Encouragingly the hybrid model outperforms a HMM, a CRF and a pure knowledge-based method to achieve an F1 of 75.37 for BF and micro average F1 of 84.01 for the whole system.
  • Nơi xuất bản: Lu���n v��n ThS. Khoa h���c m��y t��nh -- Tr�����ng �����i h���c C��ng ngh���. �����i h���c Qu���c gia H�� N���i, 2012
  • Năm xuất bản: 2012
  • Định dạng: 48 tr. + CD-ROM.
  • Ngôn ngữ: English

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