mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

Wan, Shibiao; Mak, Man‐Wai; Kung, Sun Yuan

doi:10.1016/j.jtbi.2015.06.042

Cited by 32 publications

(14 citation statements)

References 89 publications

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“…For implementation details, readers may refer to Eqs. 5 and 6 of [ 69 ]. Denote G as a GO term (either non-essential or essential) and E as an essential GO term.…”

Section: Discussionmentioning

confidence: 99%

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins

2016

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BackgroundPredicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.ResultsThis paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed.ConclusionsExperimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers’ convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

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“…For implementation details, readers may refer to Eqs. 5 and 6 of [ 69 ]. Denote G as a GO term (either non-essential or essential) and E as an essential GO term.…”

Section: Discussionmentioning

confidence: 99%

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins

2016

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“…We have first employed the Hum-mPLoc 3.0 [2] human protein dataset to compare the performance of PSL-Recommender to six methods that were in-troduced for protein localization in human. The methods include YLoc+ [6], iLoc-Hum [47], WegoLoc [48], mLASSO-Hum [49] and Hum-mPloc 3.0. The F1 − score for each location and the ACC and F1 − mean of all methods on Hum-mploc 3.0 dataset is depicted in Table 3.…”

Section: Comparison With the State-of-art Methodsmentioning

confidence: 99%

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

Jamali¹,

Eslahchi

Jahangiri

2018

Preprint

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Identifying a protein's subcellular location is of great interest for understanding its function and behavior within the cell. In the last decade, many computational approaches have been proposed as a surrogate for expensive and inefficient wet-lab methods that are used for protein subcellular localization. Yet, there is still much room for improving the prediction accuracy of these methods.PSL-Recommender (Protein subcellular location recommender) is a method that employs neighborhood regularized logistic matrix factorization to build a recommender system for protein subcellular localization. The effectiveness of PSL-Recommender method is benchmarked on one human and three animals datasets. The results indicate that the PSL-Recommender significantly outperforms state-of-the-art methods, improving the previous best method up to 31% in F1 − mean, up to 28% in ACC, and up to 47% in AVG. The source of datasets and codes are available at: https://github.com/RJamali/PSL-Recommender

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“…Внутриклеточные транспортные механизмы обеспечивают присутствие нужного белка на соответствующем сайте локализации в нужное время, позволяя организму достичь большего функционального разнообразия без дальнейшего усложнения структуры самих белков и обеспечивает их включение во внутриклеточные функциональные сети. Как отмечается в ряде работ, ошибки в работе транспортных механизмов, вызванные такими факторами как мутации, приводят к неправильному расположению белков внутри клетки, что может вызвать нарушение функционирования белков, что в свою очередь может привести к таким болезням, как рак, почечные камни, болезнь Альцгеймера [32][33][34][35]. Таким образом, сегодня определение сайтов локализации белков рассматривается как один из подходов, который позволит улучшить процесс выбора цели при разработке лекарств [31].…”

Section: задача определения внутриклеточной локализации белковunclassified

Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

Nazin¹,

Готовцев²

2019

Math.Biol.Bioinf.

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This paper describes the use of probabilistic neural networks to solve problems of bioinformatics by the example of determining the localization of proteins according to their primary structure. The data used are sets of characteristics of amino acid sequences of proteins, obtained by various software tools aimed at finding specific signal sequences, as well as data on where these proteins are localized in cells of two microorganisms — E. coli and S. cerevisiae. The data source is the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets). The possibility of using probabilistic neural networks to solve this problem is shown, since the classification accuracy of 57.5% and 85.0% for yeast and for bacterial cells is obtained, respectively. The obtained indicators of the accuracy of the classification of the data used exceed those that, according to the literature, were achieved with the use of other recognition methods. It is noted that a high learning rate and the possibility of modification make probabilistic neural networks a promising tool for analyzing bioinformatics data. In silico

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mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor

Cited by 32 publications

References 89 publications

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

Using Probabilistic Neural Networks to Predict the Localization of Proteins in Cell Compartments

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