Semi-supervised protein subcellular localization

Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang

doi:10.1186/1471-2105-10-s1-s47

Cited by 28 publications

(16 citation statements)

References 54 publications

(39 reference statements)

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“…In SubLoc, a query sequence is converted to 20-dim amino-acid composition vector for classification by SVMs. Recently, Xu et al [20] proposed a semi-supervised learning technique (a kind of transductive learning) that makes use of unlabelled test data to boost the classification performance of SVMs. One limitation of composition-based methods is that information about the sequence order is not easy to represent.…”

Section: B Approaches To Subcellular Localization Predictionmentioning

confidence: 99%

Truncation of protein sequences for fast profile alignment with application to subcellular localization

Mak

Wang

Kung

2010

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Abstract-We have recently found that the computation time of homology-based subcellular localization can be substantially reduced by aligning profiles up to the cleavage site positions of signal peptides, mitochondrial targeting peptides, and chloroplast transit peptides [1]. While the method can reduce the profile alignment time by as much as 20 folds, it cannot reduce the computation time spent on creating the profiles. In this paper, we propose a new approach that can reduce both the profile creation time and profile alignment time. In the new approach, instead of cutting the profiles, we shorten the sequences by cutting them at the cleavage site locations. The shortened sequences are then presented to PSI-BLAST to compute the profiles. Experimental results and analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without sacrificing subcellular localization accuracy. Once a pairwise profile-alignment score matrix has been obtained, a one-vs-rest SVM classifier can be trained. To further reduce the training and recognition time of the classifier, we propose a perturbation discriminant analysis (PDA) technique. It was found that PDA enjoys a short training time as compared to the conventional SVM.

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Section: B Approaches To Subcellular Localization Predictionmentioning

confidence: 99%

Truncation of protein sequences for fast profile alignment with application to subcellular localization

Mak

Wang

Kung

2010

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…The first group is based on the basic machine learning algorithms, such as neural network [3], k-nearest neighbor (KNN) [5], support vector machine [6], [13], [14], and so forth. The other group contains several novel machine learning paradigms, such as ensemble learning [15], [16], semi-supervised learning [17], multitask learning [18], transfer learning [19], boosted association rules [20], and so on, which substantially improve the performance of prediction of subcellular localization of proteins.…”

Section: Introductionmentioning

confidence: 99%

Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction

Wang¹,

Li²

2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multilocation proteins to multiple proteins with single location, which does not take correlations among different subcellular locations into account. In this paper, a novel method named random label selection (RALS) (multilabel learning via RALS), which extends the simple binary relevance (BR) method, is proposed to learn from multilocation proteins in an effective and efficient way. RALS does not explicitly find the correlations among labels, but rather implicitly attempts to learn the label correlations from data by augmenting original feature space with randomly selected labels as its additional input features. Through the fivefold cross-validation test on a benchmark data set, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark data sets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multilocations of proteins. The prediction web server is available at >http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

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“…Semi-supervised learning has mostly been studied on protein classification (Weston et al, 2005;Weston et al, 2006;Kall et al, 2007;Craig and Liao, 2007;Xu et al, 2009) and efforts on semi-supervised learning from imbalanced distributions have focused on protein datasets with relatively low imbalance degrees. For example, Kondratovich et al (2013) address the problem of molecule activity prediction and they experiment with ten relatively small (3,000 instances) molecule activity datasets with imbalance degrees no larger than to 1-to-40.…”

Section: Related Workmentioning

confidence: 99%

An empirical study of self-training and data balancing techniques for splice site prediction

Stănescu

Caragea

2017

IJBRA

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Thanks to Next Generation Sequencing technologies, unlabelled data is now generated easily, while the annotation process remains expensive. Semi-supervised learning represents a cost-effective alternative to supervised learning, as it can improve supervised classifiers by making use of unlabelled data. However, semi-supervised learning has not been studied much for problems with highly skewed class distributions, which are prevalent in bioinformatics. To address this limitation, we carry out a study of a semi-supervised learning algorithm, specifically self-training based on Naïve Bayes, with focus on data-level approaches for handling imbalanced class distributions. Our study is conducted on the problem of predicting splice sites and it is based on datasets for which the ratio of positive to negative examples is 1-to-99. Our results show that under certain conditions semi-supervised learning algorithms are a better choice than purely supervised classification algorithms.

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Semi-supervised protein subcellular localization

Cited by 28 publications

References 54 publications

Truncation of protein sequences for fast profile alignment with application to subcellular localization

Truncation of protein sequences for fast profile alignment with application to subcellular localization

Multilabel Learning via Random Label Selection for Protein Subcellular Multilocations Prediction

An empirical study of self-training and data balancing techniques for splice site prediction

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