A New Avenue for Classification and Prediction of Olive Cultivars Using Supervised and Unsupervised Algorithms

Beiki, A H; Saboor, Saba; Ebrahimi, Mansour

doi:10.1371/journal.pone.0044164

Cited by 32 publications

(23 citation statements)

References 52 publications

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“…Each attribute 346 weighting system uses a specific pattern to define the most important SSR fragment 347 alleles. Thus, the results may be different (Baumgartner et al, 2010), as has been 348 highlighted in previous studies (Ashrafi et al, 2011;Beiki et al, 2012;Ebrahimi et al 349 2011). The results showed that attribute subset selection can be beneficiary both to 350 processing time and getting more accurate results.…”

Section: Data Cleaning Deals With Detecting and Removing Errors And Imentioning

confidence: 78%

“…data and were used to remove redundancy and co-linearity, useless or duplicated 343 attributes in order to improve the quality of data which results in a smaller database 344 (Ashrafi et al, 2011;Beiki et al, 2012). More than 7% of the attribute alleles 345 discarded when these algorithms were applied on the original dataset.…”

Section: Data Cleaning Deals With Detecting and Removing Errors And Imentioning

confidence: 99%

“…The goal of unsupervised pattern is to identify small subsets of alleles that 363 display coordinated expression patterns. The unsupervised algorithm figure out the 364 underlying similarities among a set of feature vectors, and to cluster similar vectors 365 together (Ashrafi, Alemzadeh, Ebrahimi, Ebrahimie and Dadkhodaei 2011, Beiki, 366 Saboor and Ebrahimi 2012, Garcia-Gonzalez et al 2010, while supervised pattern 367 like decision trees are very popular tools for classification (Migliorini et al 2013, Yun 368 et al 2014. In unsupervised learning, the machine finds new patterns without 369 relying on prior training examples, usually by using a set of pre-defined rules (Bravo 370 and Medina 2008, El Harchaoui et al 2013, Handfield et al 2013, Markovich Gordon 371 et al 2012).…”

Section: Data Cleaning Deals With Detecting and Removing Errors And Imentioning

confidence: 99%

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Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

Beiki

2019

Preprint

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Learning Approaches 2Drought is one of the prime abiotic stresses in the world. Now, amongst the new 3 technologies available for speed up the releasing of new drought tolerance 4 genotypes, there is an emanate discipline called machine learning. The study 5 presents Machine Learning for identification, classification and prediction of drought 6 tolerance maize inbred lines based on SSR genetic markers datasets generated from 7 PCR reactions. A total of 356 SSR reproducible fragment alleles were detected 8 across the 71 polymorphic SSR loci. A dataset of 12 inbred lines with these fragments 9 prepared as attributes and was imported into RapidMiner software. After removal of 10 duplicates, useless and correlated features, 311 feature attributes were polymorphic, 11 ranging in size from 1500 to 3500 bp. The most important attribute fragment alleles in 12 different attribute weighting selected. Ten datasets created using attribute selection 13 (weighting) algorithms. Different classification algorithms were applied on datasets. 14 These can be used to identify groups of alleles with similar patterns of expression, 15and are able to create some models that have been applied successfully in the 16 prediction, classification and pattern recognition in drought stress. Some 17 unsupervised models were able to differentiate tolerant inbred lines from susceptible. 18Four unsupervised models were able to produce the different decision trees with root 19 and leaves. The most important attribute alleles almost in all of models were 20 phi033a3, bnlg1347a1 and bnlg172a2 respectively, that can help to identify tolerant 21 maize inbred lines with high precision. 22 23

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Section: Data Cleaning Deals With Detecting and Removing Errors And Imentioning

confidence: 78%

Section: Data Cleaning Deals With Detecting and Removing Errors And Imentioning

confidence: 99%

Section: Data Cleaning Deals With Detecting and Removing Errors And Imentioning

confidence: 99%

See 1 more Smart Citation

Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

Beiki

2019

Preprint

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“…These mentioned approaches have been taken to specify important structural attributes, prediction and classification of protein thermo-stability (Ebrahimi et al 2009), P glycoprotein pump (Hammann et al 2009) halo-stability , olive cultivars (Beiki et al 2012), α-linolenic acid content (Zinati et al 2014) as well as genotype discrimination (Nasiri et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis

Zinati

Alemzadeh

Kayvanjoo

2016

Physiol Mol Biol Plants

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As an extended gamut of integral membrane (extrinsic) proteins, and based on their transporting specificities, P-type ATPases include five subfamilies in Arabidopsis, inter alia, P4ATPases (phospholipid-transporting ATPase), P3AATPases (plasma membrane H(+) pumps), P2A and P2BATPases (Ca(2+) pumps) and P1B ATPases (heavy metal pumps). Although, many different computational methods have been developed to predict substrate specificity of unknown proteins, further investigation needs to improve the efficiency and performance of the predicators. In this study, various attribute weighting and supervised clustering algorithms were employed to identify the main amino acid composition attributes, which can influence the substrate specificity of ATPase pumps, classify protein pumps and predict the substrate specificity of uncharacterized ATPase pumps. The results of this study indicate that both non-reduced coefficients pertaining to absorption and Cys extinction within 280 nm, the frequencies of hydrogen, Ala, Val, carbon, hydrophilic residues, the counts of Val, Asn, Ser, Arg, Phe, Tyr, hydrophilic residues, Phe-Phe, Ala-Ile, Phe-Leu, Val-Ala and length are specified as the most important amino acid attributes through applying the whole attribute weighting models. Here, learning algorithms engineered in a predictive machine (Naive Bays) is proposed to foresee the Q9LVV1 and O22180 substrate specificities (P-type ATPase like proteins) with 100 % prediction confidence. For the first time, our analysis demonstrated promising application of bioinformatics algorithms in classifying ATPases pumps. Moreover, we suggest the predictive systems that can assist towards the prediction of the substrate specificity of any new ATPase pumps with the maximum possible prediction confidence.

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“…This classifier has been widely used before (for more details see (West 2003 Baseri et al 2011). Two models of Naïve Bayse (returns classification model using estimated normal distributions) and Naïve bayse kernel (returns classification model using estimated kernel densities) (Beiki et al 2012) used and the model accuracy in predicting the type of lung tumor calculted.…”

Section: Methodsmentioning

confidence: 99%

Prediction of lung tumor types based on protein attributes by machine learning algorithms

et al. 2013

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Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-2-238) contains supplementary material, which is available to authorized users.

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A New Avenue for Classification and Prediction of Olive Cultivars Using Supervised and Unsupervised Algorithms

Cited by 32 publications

References 52 publications

Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis

Prediction of lung tumor types based on protein attributes by machine learning algorithms

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