A Framework for Space-Efficient String Kernels

Belazzougui, Djamal; Cunial, Fabio

doi:10.1007/s00453-017-0286-4

Cited by 12 publications

(1 citation statement)

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“…most of such k -mers are not likely to contain sequencing errors. Such k can be computed within the same time and space budget as the algorithms in this paper, using the algorithm described in [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

A framework for space-efficient read clustering in metagenomic samples

et al. 2017

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BackgroundA metagenomic sample is a set of DNA fragments, randomly extracted from multiple cells in an environment, belonging to distinct, often unknown species. Unsupervised metagenomic clustering aims at partitioning a metagenomic sample into sets that approximate taxonomic units, without using reference genomes. Since samples are large and steadily growing, space-efficient clustering algorithms are strongly needed.ResultsWe design and implement a space-efficient algorithmic framework that solves a number of core primitives in unsupervised metagenomic clustering using just the bidirectional Burrows-Wheeler index and a union-find data structure on the set of reads. When run on a sample of total length n, with m reads of maximum length ℓ each, on an alphabet of total size σ, our algorithms take O(n(t+logσ)) time and just 2n+o(n)+O(max{ℓ σlogn,K logm}) bits of space in addition to the index and to the union-find data structure, where K is a measure of the redundancy of the sample and t is the query time of the union-find data structure.ConclusionsOur experimental results show that our algorithms are practical, they can exploit multiple cores by a parallel traversal of the suffix-link tree, and they are competitive both in space and in time with the state of the art.

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Section: Methodsmentioning

confidence: 99%

A framework for space-efficient read clustering in metagenomic samples

et al. 2017

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The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Găman

Ionescu

2021

Int J of Intelligent Sys

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Motivated by the seemingly high accuracy levels of machine learning (ML) models in Moldavian versus Romanian dialect identification and the increasing research interest on this topic, we provide a follow-up on the Moldavian versus Romanian Cross-Dialect Topic Identification (MRC) shared task of the VarDial 2019 evaluation campaign. The shared task included two subtask types: one that consisted in discriminating between the Moldavian and Romanian dialects and one that consisted in classifying documents by topic across the two dialects of Romanian. Participants achieved impressive scores, for example, the top model for Moldavian versus Romanian dialect identification obtained a macro-F 1 score of 0.895. We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared with ML models. Hence, it remains unclear why the methods proposed by participants attain such high accuracy rates. Our goal is to understand (i) why the proposed methods work so well (by visualizing the discriminative features) and (ii) to what extent these methods can keep their high accuracy levels, for example, when we shorten the text samples to single sentences or when we use tweets at inference time. A secondary goal of our

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