MotifMiner: A Table Driven Greedy Algorithm for DNA Motif Mining

Seeja, K. R.; Alam, M. Afshar; Jain, Sonia

doi:10.1007/978-3-642-04020-7_42

Cited by 1 publication

(1 citation statement)

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“…Identification of recurrent sub-graphs from benchmark DAGs is similar in essence to the Motif discovery problem [28] which is famous because of its application in DNA fingerprinting [20]. This is a very active area of research and recently we have seen some attempts to use sampling [10], machine learning [21] and distributed algorithms [22,6,29] to compute the Motifs (statistically significant recurrent sub-graphs) as quickly as possible. Our DAGs, on the other hand, have labeled nodes (labeled with operation types) and our motifs have to account for the fact that some opperations are commutative but not others, which makes direct translation to Motif discovery problem more difficult.…”

Section: Related Workmentioning

confidence: 99%

SWAPPER: A framework for automatic generation of formula simplifiers based on conditional rewrite rules

Singh¹,

Solar-Lezama²

2016

2016 Formal Methods in Computer-Aided Design (FMCAD)

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This paper addresses the problem of creating simplifiers for logic formulas based on conditional term rewriting. In particular, the paper focuses on a program synthesis application where formula simplifications have been shown to have a significant impact. We show that by combining machine learning techniques with constraint-based synthesis, it is possible to synthesize a formula simplifier fully automatically from a corpus of representative problems, making it possible to create formula simplifiers tailored to specific problem domains. We demonstrate the benefits of our approach for synthesis benchmarks from the SyGuS competition and automated grading.

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