अमूर्त

A Sequence model based phenotype structure discovery algorithm

Yu-Hai Zhao, Ying Yin


Phenotype structure discovery is one of the most important problem in microarray data analysis. The goal is to (1) find groups of samples corresponding to different phenotypes (such as disease or normal), and (2) for each group of samples,find the representative expression pattern that distinguishes this group from others. Different from the existing singleton discriminability based approach and combination discriminability-based approach, we present a novel method in this paper. Based on the proposed g*-sequence model, an efficient algorithm, namely FINDER, is developed to mine the optimal phenotype structure from a given dataset. Further, several effective pruning strategies are designed to improve the efficiency. The experiments conducted on both synthetic and real microarray datasets show that the phenotype structures discovered by FINDER are of both statistical and biological significance. Moreover, FINDER is 2~3 orders of magnitude faster than the alternatives.


अस्वीकृति: इस सारांश का अनुवाद कृत्रिम बुद्धिमत्ता उपकरणों का उपयोग करके किया गया है और इसे अभी तक समीक्षा या सत्यापित नहीं किया गया है।

में अनुक्रमित

  • कैस
  • गूगल ज्ञानी
  • जे गेट खोलो
  • चीन राष्ट्रीय ज्ञान अवसंरचना (सीएनकेआई)
  • उद्धरण कारक
  • ब्रह्मांड IF
  • रिसर्च जर्नल इंडेक्सिंग की निर्देशिका (डीआरजेआई)
  • गुप्त खोज इंजन लैब्स
  • विद्वान लेख प्रभाव कारक (SAJI))
  • आईसीएमजेई

और देखें

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