FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1748330191> ?p ?o ?g. }

• W1748330191 endingPage "276" @default.
• W1748330191 startingPage "263" @default.
• W1748330191 abstract "The biclustering problem has been extensively studied in many areas including e-commerce, data mining, machine learning, pattern recognition, statistics, and more recently in computational biology. Given an n ×m matrix A (n ≥ m), the main goal of biclustering is to identify a subset of rows (called objects) and a subset of columns (called properties) such that some objective function that specifies the quality of the found bicluster (formed by the subsets of rows and of columns of A) is optimized. The problem has been proved or conjectured to be NP-hard under various mathematical models. In this paper, we study a probabilistic model of the implanted additive bicluster problem, where each element in the n×m background matrix is a random number from [0, L − 1], and a k×k implanted additive bicluster is obtained from an error-free additive bicluster by randomly changing each element to a number in [0, L − 1] with probability θ. We propose an O(n 2 m) time voting algorithm to solve the problem. We show that for any constant δ such that ((1-delta)(1-theta)^2 -frac 1 L >0), when (k ge max left{frac 8 alpha sqrt{nlog n},~ frac {8 log n} c + log (2L)right}), where c is a constant number, the voting algorithm can correctly find the implanted bicluster with probability at least (1 - frac{9}{n^{2}}). We also implement our algorithm as a software tool for finding novel biclusters in microarray gene expression data, called VOTE. The implementation incorporates several nontrivial ideas for estimating the size of an implanted bicluster, adjusting the threshold in voting, dealing with small biclusters, and dealing with multiple (and overlapping) implanted biclusters. Our experimental results on both simulated and real datasets show that VOTE can find biclusters with a high accuracy and speed.KeywordsbiclusterChernoff boundpolynomial-time algorithmprobability modelcomputational biologygene expression data analysis" @default.
• W1748330191 created "2016-06-24" @default.
• W1748330191 creator A5016038454 @default.
• W1748330191 creator A5065315257 @default.
• W1748330191 creator A5074292548 @default.
• W1748330191 creator A5074903674 @default.
• W1748330191 date "2008-06-07" @default.
• W1748330191 modified "2023-09-23" @default.
• W1748330191 title "Finding Additive Biclusters with Random Background" @default.
• W1748330191 cites W2047386086 @default.
• W1748330191 cites W2057682468 @default.
• W1748330191 cites W2062307640 @default.
• W1748330191 cites W2078758709 @default.
• W1748330191 cites W2105883975 @default.
• W1748330191 cites W2115875363 @default.
• W1748330191 cites W2126877147 @default.
• W1748330191 cites W2130095365 @default.
• W1748330191 cites W2130158951 @default.
• W1748330191 cites W2136107412 @default.
• W1748330191 cites W2137102738 @default.
• W1748330191 cites W2137683543 @default.
• W1748330191 cites W2140863273 @default.
• W1748330191 cites W2143274936 @default.
• W1748330191 cites W2144544802 @default.
• W1748330191 cites W2150718242 @default.
• W1748330191 cites W2151763757 @default.
• W1748330191 cites W2158128075 @default.
• W1748330191 cites W2166602562 @default.
• W1748330191 cites W4231916799 @default.
• W1748330191 cites W4242399520 @default.
• W1748330191 doi "https://doi.org/10.1007/978-3-540-69068-9_25" @default.
• W1748330191 hasPublicationYear "2008" @default.
• W1748330191 type Work @default.
• W1748330191 sameAs 1748330191 @default.
• W1748330191 citedByCount "1" @default.
• W1748330191 countsByYear W17483301912015 @default.
• W1748330191 crossrefType "book-chapter" @default.
• W1748330191 hasAuthorship W1748330191A5016038454 @default.
• W1748330191 hasAuthorship W1748330191A5065315257 @default.
• W1748330191 hasAuthorship W1748330191A5074292548 @default.
• W1748330191 hasAuthorship W1748330191A5074903674 @default.
• W1748330191 hasBestOaLocation W17483301912 @default.
• W1748330191 hasConcept C105795698 @default.
• W1748330191 hasConcept C106487976 @default.
• W1748330191 hasConcept C11413529 @default.
• W1748330191 hasConcept C114614502 @default.
• W1748330191 hasConcept C118615104 @default.
• W1748330191 hasConcept C121332964 @default.
• W1748330191 hasConcept C135598885 @default.
• W1748330191 hasConcept C14036430 @default.
• W1748330191 hasConcept C144817290 @default.
• W1748330191 hasConcept C158693339 @default.
• W1748330191 hasConcept C17744445 @default.
• W1748330191 hasConcept C185592680 @default.
• W1748330191 hasConcept C199360897 @default.
• W1748330191 hasConcept C199539241 @default.
• W1748330191 hasConcept C2777027219 @default.
• W1748330191 hasConcept C33704608 @default.
• W1748330191 hasConcept C33923547 @default.
• W1748330191 hasConcept C41008148 @default.
• W1748330191 hasConcept C43617362 @default.
• W1748330191 hasConcept C520049643 @default.
• W1748330191 hasConcept C62520636 @default.
• W1748330191 hasConcept C64812099 @default.
• W1748330191 hasConcept C73555534 @default.
• W1748330191 hasConcept C77088390 @default.
• W1748330191 hasConcept C78458016 @default.
• W1748330191 hasConcept C86803240 @default.
• W1748330191 hasConcept C94625758 @default.
• W1748330191 hasConcept C94641424 @default.
• W1748330191 hasConceptScore W1748330191C105795698 @default.
• W1748330191 hasConceptScore W1748330191C106487976 @default.
• W1748330191 hasConceptScore W1748330191C11413529 @default.
• W1748330191 hasConceptScore W1748330191C114614502 @default.
• W1748330191 hasConceptScore W1748330191C118615104 @default.
• W1748330191 hasConceptScore W1748330191C121332964 @default.
• W1748330191 hasConceptScore W1748330191C135598885 @default.
• W1748330191 hasConceptScore W1748330191C14036430 @default.
• W1748330191 hasConceptScore W1748330191C144817290 @default.
• W1748330191 hasConceptScore W1748330191C158693339 @default.
• W1748330191 hasConceptScore W1748330191C17744445 @default.
• W1748330191 hasConceptScore W1748330191C185592680 @default.
• W1748330191 hasConceptScore W1748330191C199360897 @default.
• W1748330191 hasConceptScore W1748330191C199539241 @default.
• W1748330191 hasConceptScore W1748330191C2777027219 @default.
• W1748330191 hasConceptScore W1748330191C33704608 @default.
• W1748330191 hasConceptScore W1748330191C33923547 @default.
• W1748330191 hasConceptScore W1748330191C41008148 @default.
• W1748330191 hasConceptScore W1748330191C43617362 @default.
• W1748330191 hasConceptScore W1748330191C520049643 @default.
• W1748330191 hasConceptScore W1748330191C62520636 @default.
• W1748330191 hasConceptScore W1748330191C64812099 @default.
• W1748330191 hasConceptScore W1748330191C73555534 @default.
• W1748330191 hasConceptScore W1748330191C77088390 @default.
• W1748330191 hasConceptScore W1748330191C78458016 @default.
• W1748330191 hasConceptScore W1748330191C86803240 @default.
• W1748330191 hasConceptScore W1748330191C94625758 @default.
• W1748330191 hasConceptScore W1748330191C94641424 @default.

• next