Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters

Lan, Haidong; Chan, Yuandong; Xu, Kai; Schmidt, Bertil; Peng, Shaoliang; Liu, Weiguo

doi:10.1186/s12859-016-1128-0

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…In Table 2, the final experimental results are expressed as the average of 100 five-fold cross-validation. It is worth noting that AUC is known to be insensitive to skewed class distributions [38]. Considering that the dataset used in this paper is highly unbalanced, there are more negative factors than positive ones.…”

Section: Resultsmentioning

confidence: 99%

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

et al. 2019

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Background: Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed. Results: A simple and effective method, known as Nearest Profile-based Collaborative Matrix Factorization (NPCMF), is proposed to identify novel MDAs. The nearest profile is introduced to our method to achieve the highest AUC value compared with other advanced methods. For some miRNAs and diseases without any association, we use the nearest neighbour information to complete the prediction. Conclusions: To evaluate the performance of our method, five-fold cross-validation is used to calculate the AUC value. At the same time, three disease cases, gastric neoplasms, rectal neoplasms and colonic neoplasms, are used to predict novel MDAs on a gold-standard dataset. We predict the vast majority of known MDAs and some novel MDAs. Finally, the prediction accuracy of our method is determined to be better than that of other existing methods. Thus, the proposed prediction model can obtain reliable experimental results.

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Section: Resultsmentioning

confidence: 99%

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

et al. 2019

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“…Sometimes, Levenshtein distance is also referred as edit distance between two strings. Edit distance find its applications in natural language processing where spell correction is most common use of it and in computational biology it is used C omparison of two strings helps in solving problems from many domains including bioinformatics (DNA analysis) [1], textprocessing (spell-checkers, plagiarism detection, and error correction), signal processing, information retrieval, speech recognition, and web mining. String matching or string comparison comes into different forms: finding if a string is substring of another string, identifying the longest common subsequence, and checking how similar or dissimilar two strings are [2].…”

Section: Introductionmentioning

confidence: 99%