Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology

Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

doi:10.1016/j.jtbi.2014.04.040

Cited by 52 publications

(37 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tenfold cross-validation was performed to provide evidence of the accuracy of the classifier model to correctly predict the AR classes generated by ROC analysis (42)(43)(44). To this end, we employed supervised learning to build a range of classifiers including Decision Stump, Decision Tree, Decision Tree with information gain, and Na€ ve Bayes from cohort features such as tumor size and overall survival.…”

Section: Tenfold Cross-validationmentioning

confidence: 99%

The Magnitude of Androgen Receptor Positivity in Breast Cancer Is Critical for Reliable Prediction of Disease Outcome

Ricciardelli

Bianco‐Miotto

Jindal

et al. 2018

Clinical Cancer Research

Self Cite

View full text Add to dashboard Cite

Purpose: Consensus is lacking regarding the androgen receptor (AR) as a prognostic marker in breast cancer. The objectives of this study were to comprehensively review the literature on AR prognostication and determine optimal criteria for AR as an independent predictor of breast cancer survival.Experimental Design: AR positivity was assessed by immunostaining in two clinically validated primary breast cancer cohorts [training cohort, n ¼ 219; validation cohort, n ¼ 418; 77% and 79% estrogen receptor alpha (ERa) positive, respectively]. The optimal AR cut-point was determined by ROC analysis in the training cohort and applied to both cohorts.Results: AR was an independent prognostic marker of breast cancer outcome in 22 of 46 (48%) previous studies that performed multivariate analyses. Most studies used cut-points of 1% or 10% nuclear positivity. Herein, neither 1% nor 10% cut-points were robustly prognostic. ROC analysis revealed that a higher AR cut-point (78% positivity) provided optimal sensitivity and specificity to predict breast cancer survival in the training (HR, 0.41; P ¼ 0.015) and validation (HR, 0.50; P ¼ 0.014) cohorts. Tenfold cross-validation confirmed the robustness of this AR cut-point. Patients with ERa-positive tumors and AR positivity !78% had the best survival in both cohorts (P < 0.0001). Among the combined ERa-positive cases, those with comparable or higher levels of AR (AR:ERa-positivity ratio >0.87) had the best outcomes (P < 0.0001).Conclusions: This study defines an optimal AR cut-point to reliably predict breast cancer survival. Testing this cut-point in prospective cohorts is warranted for implementation of AR as a prognostic factor in the clinical management of breast cancer.

show abstract

Section: Tenfold Cross-validationmentioning

confidence: 99%

The Magnitude of Androgen Receptor Positivity in Breast Cancer Is Critical for Reliable Prediction of Disease Outcome

Ricciardelli

Bianco‐Miotto

Jindal

et al. 2018

Clinical Cancer Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…In 2014, neural network and SVM classifier were used to predict lipid binding proteins by Bakhtiarizadeh et al [4]; the experiments showed that SVM was more successful at discriminating between LBPs and non-LBPs than neural network. In 2016, the potential druggable proteins were predicted through comparing 6 kinds of machine learning algorithms by Jamali et al; the experiments showed that neural network was the best classifier when predicting potential druggable proteins [5].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

Wang

Junmei

Wang

et al. 2017

BioMed Research International

View full text Add to dashboard Cite

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

show abstract

“…Sherif et al [14] used several computational methods to identify genetic signatures characteristic of the HA gene of swine, human and bird viral strains. Application of supervised data mining has opened a new avenue for better understanding of diseases, gene expression, protein behavior, drug design and performance, and molecular marker discovery [4, 15–25]. In particular, association rule mining is an effective method that has the potential to discover interesting and previously hidden relationships between items in a dataset [26].…”

Section: Introductionmentioning

confidence: 99%

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments

Kargarfard

Sami

Mohammadi‐Dehcheshmeh

et al. 2016

BMC Genomics

Self Cite

View full text Add to dashboard Cite

BackgroundRecent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range.MethodsTo obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment.ResultWe found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions.ConclusionHost range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3250-9) contains supplementary material, which is available to authorized users.

show abstract

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology

Cited by 52 publications

References 79 publications

The Magnitude of Androgen Receptor Positivity in Breast Cancer Is Critical for Reliable Prediction of Disease Outcome

The Magnitude of Androgen Receptor Positivity in Breast Cancer Is Critical for Reliable Prediction of Disease Outcome

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments

Contact Info

Product

Resources

About