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AAAI02-155.pdf
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🔗 Source: cdn.aaai.org
📊 Size: 528 KB
👤 Author: Zhou, Rong; Hansen, Eric A.
⬇️ Downloads: 174

Summary

Multiple sequence alignment is a central problem in computational biology, where gaps are inserted into sequences to shift characters to matching positions and a scoring function is used to rank the biological plausibility of alignments. The multiple sequence alignment problem can be formalized as a shortest-path problem through a d-dimensional lattice. Dynamic programming and A are traditional approaches, but A has a large branching factor, making it difficult to solve. Two solutions, A with Partial Expansion (PEA) and Enhanced A (EA), have been proposed to reduce the size of the open list. This paper describes a third approach, Anytime A, which uses weighted A search to quickly find a solution that provides an upper bound to prune the open list, and continues to find improved solutions that converge to an optimal solution. Experimental results show that Anytime A is more memory-efficient than PEA and EA* in aligning dissimilar sequences, and offers a tradeoff between solution quality and computation time.

Description

Multiple sequence alignment is a central problem in computational biology, where gaps are inserted into sequences to shift characters to matching positions and...

Technical Information

  • File Format: PDF
  • File Size: 528 KB
  • Pages: 2
  • Language: EN
  • Author: Zhou, Rong; Hansen, Eric A.
  • Total Downloads: 174
  • Last Updated: 7 days ago

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