ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Wei, Leyi; Zhou, Chen; Chen, Huangrong; Song, Jiangning; Su, Ran

doi:10.1093/bioinformatics/bty451

Cited by 366 publications

(270 citation statements)

References 33 publications

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“…This phase was addressed by analyzing the generic image in its entirety; a set of features are extracted from the entire image and these are used by an SVM classifier, in order to associate the generic image with the positive/negative classes. SVM has been widely used in biomedical research [27][28][29][30][31][32] and this is certainly linked to the results of the classification, but also to the advantage that this type of classifier depends on a few parameters.…”

Section: Fluorescence Intensity Classificationmentioning

confidence: 99%

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An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

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The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

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Section: Fluorescence Intensity Classificationmentioning

confidence: 99%

“…There are many methods proposed in the literature for the decrease in dimensionality, used in supervised or unsupervised classification problems, such as sequential forward search [31,48], random forest algorithm [49,50], and ANOVA feature selection [51].…”

Section: Features Selection Based On Ldamentioning

confidence: 99%

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

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“…Three cross-validation methods, that is, independent dataset test, sub-sampling (or K-fold cross-validation) test and jackknife test, are often used to evaluate the anticipated success rate of a predictor. Among the three methods, however, the jackknife test is deemed the least arbitrary and most objective [34] and hence has been widely recognized and increasingly adopted by investigators to examine the quality of various predictors [35][36][37][38][39]. However, this procedure is time-and source-consuming.…”

Section: Datamentioning

confidence: 99%

A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects

Baniata

Park

2018

Applied Sciences

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The statistical machine translation for the Arabic language integrates external linguistic resources such as part-of-speech tags. The current research presents a Bidirectional Long Short-Term Memory (Bi-LSTM) - Conditional Random Fields (CRF) segment-level Arabic Dialect POS tagger model, which will be integrated into the Multitask Neural Machine Translation (NMT) model. The proposed solution for NMT is based on the recurrent neural network encoder-decoder NMT model that has been introduced recently. The study has proposed and developed a unified Multitask NMT model that shares an encoder between the two tasks; Arabic Dialect (AD) to Modern Standard Arabic (MSA) translation task and the segment-level POS tagging tasks. A shared layer and an invariant layer are shared between the translation tasks. By training translation tasks and POS tagging task alternately, the proposed model can leverage the characteristic information and improve the translation quality from Arabic dialects to Modern Standard Arabic. The experiments are conducted from Levantine Arabic (LA) to MSA and Maghrebi Arabic (MA) to MSA translation tasks. As an additional linguistic resource, the segment-level part-of-speech tags for Arabic dialects were also exploited. Experiments suggest that translation quality and the performance of POS tagger were improved with the implementation of multitask learning approach.

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“…Classification models have been published for the prediction of protein activities such as anti-angiogenic [13], anti-cancer [14], enzyme class [15], epitope [16], signaling [17], lectins [18], anti-oxidant [19], and druggability [12, 20–26]. Therefore, the aim of this study was to build an effective machine learning classifier to predict druggability of breast cancer (BC) proteins, cancer-driving proteins and RNA-binding proteins (RBPs).…”

Section: Introductionmentioning

confidence: 99%

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

López‐Cortés

Cabrera-Andrade

Cruz-Segundo

et al. 2019

Preprint

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BackgroundDruggable proteins are a trending topic in drug design. The druggable proteome can be defined as the percentage of proteins that have the capacity to bind an antibody or small molecule with adequate chemical properties and affinity. The screening and in silico modeling are critical activities for the reduction of experimental costs.MethodsThe current work proposes a unique prediction model for druggable proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. After feature selection, the best classifier was obtained using the support vector machine method and 200 tri-amino acid composition descriptors.ResultsThe high performance of the model is determined by an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and accuracy of 0.929 ± 0.006 (3-fold cross-validation). Regarding the prediction of cancer-associated proteins using this model, the best ranked druggable predicted proteins in the breast cancer protein set were CDK4, AP1S1, POLE, HMMR, RPL5, PALB2, TIMP1, RPL22, NFKB1 and TOP2A; in the cancer-driving protein set were TLL2, FAM47C, SAGE1, HTR1E, MACC1, ZFR2, VMA21, DUSP9, CTNNA3 and GABRG1; and in the RNA-binding protein set were PLA2G1B, CPEB2, NOL6, LRRC47, CTTN, CORO1A, SCAF11, KCTD12, DDX43 and TMPO.ConclusionsThis powerful model predicts several druggable proteins which should be deeply studied to find better therapeutic targets and thus improve clinical trials. The scripts are freely available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

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ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 366 publications

References 33 publications

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

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