Nuclear Norm Clustering: a promising alternative method for clustering tasks

Wang, Yi; Li, Yang; Qiao, Chunhong; Liu, Xiaoyu; Meng, Hao; Shugart, Yin Yao; Xiong, Momiao; Li, Jin

doi:10.1038/s41598-018-29246-4

Cited by 5 publications

(2 citation statements)

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“…The choice of a good basis is essential for the reliability of the metrics, so we chose the GOLD/Brown base from ASTRAL/SCOPe [30]. For the validation of clusters, we used F1-Score, an external metric that provides the balance between the accuracy and sensitivity measures [31, 32]. The GOLD database - a collection “gold standard” of enzymes families experimentally validated [33] totalizing 866 sequences - to evaluation of clusters generated for RAFTS 3 G compared to three highlighted methods .…”

Section: Resultsmentioning

confidence: 99%

RAFTS3G: an efficient and versatile clustering software to analyses in large protein datasets

et al. 2019

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Background Clustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials. Results Here we present a new approach to Data Mining in large protein sequences datasets, the Rapid Alignment Free Tool for Sequences Similarity Search to Groups (RAFTS 3 G), a method to clustering aiming of losing less biological information in the processes of generation groups. The strategy developed in our algorithm is optimized to be more astringent which reflects increase in accuracy and sensitivity in the generation of clusters in a wide range of similarity. RAFTS 3 G is the better choice compared to three main methods when the user wants more reliable result even ignoring the ideal threshold to clustering. Conclusion In general, RAFTS 3 G is able to group up to millions of biological sequences into large datasets, which is a remarkable option of efficiency in clustering. RAFTS 3 G compared to other “standard-gold” methods in the clustering of large biological data maintains the balance between the reduction of biological information redundancy and the creation of consistent groups. We bring the binary search concept applied to grouped sequences which shows maintaining sensitivity/accuracy relation and up to minimize the time of data generated with RAFTS 3 G process. Electronic supplementary material The online version of this article (10.1186/s12859-019-2973-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

RAFTS3G: an efficient and versatile clustering software to analyses in large protein datasets

et al. 2019

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“…Tahapan dalam menggunakan metode AGNES adalah dengan mengambil setiap amatan sebagai klaster, kemudian klaster tersebut digabungkan secara bertahap dengan kriteria tertentu. Sedangkan DIANA menempatkan semua amatan dalam sebuah klaster dan membaginya menjadi klaster yang lebih kecil hingga diperoleh jumlah klaster yang diinginkan (Wang et al, 2018). Pada saat menentukan jarak pada klaster, terdapat beberapa metode yang digunakan, yaitu dengan single linkage, complete linkage, average linkage, dan ward.…”

Section: Gambar 1 Alur Analisis Data Hierarchical Clusteringunclassified