Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka

doi:10.3390/ijms12128415

Cited by 8 publications

(9 citation statements)

References 31 publications

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“…Overall, SVM s are intuitive, theoretically well- founded, and have been shown to be practically applicable. The methods have been widely employed by researchers in different areas of science [83] – [88] , including influenza research [89] – [93] . In general, the RBF kernel is a reasonable first choice in SVM approaches.…”

Section: Discussionmentioning

confidence: 99%

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

et al. 2014

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The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

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Section: Discussionmentioning

confidence: 99%

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

et al. 2014

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“…Quantitative structure-activity relationship (QSAR), which is a well-recognized tool for estimating chemical activities, has been widely applied for bioactive peptides prediction [ 5 ]. The QSAR models have been successfully built on ACE-inhibitory peptides [ 6 ], antimicrobial peptides [ 7 ], antioxidant peptides [ 8 , 9 , 10 ], antitumor peptides [ 11 ], bitter peptides [ 12 ], and etc. The QSAR study of antioxidant peptides mainly focused on di and tripeptides, because they can be absorbed intact from the intestinal lumen into the bloodstream and then produce biological effects at the tissue level [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Deng

Long

Tang

et al. 2019

IJMS

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Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q2) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

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“…SVMs are theoretically well-established intuitive and feasible techniques for classification and prediction of supervised data [ 42 – 47 ]. We used seven SVMs models to classify and predict the HCV treatment response.…”

Section: Methodsmentioning

confidence: 99%

Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

2014

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BackgroundHepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world population and remains one of the health threatening human viruses, worldwide. In the absence of an effective vaccine, therapeutic approach is the only option to combat hepatitis C. Interferon-alpha (IFN-alpha) and ribavirin (RBV) combination alone or in combination with recently introduced new direct-acting antivirals (DAA) is used to treat patients infected with HCV. The present study utilized feature selection methods (Gini Index, Chi Squared and machine learning algorithms) and other bioinformatics tools to identify genetic determinants of therapy outcome within the entire HCV nucleotide sequence.ResultsUsing combination of several algorithms, the present study performed a comprehensive bioinformatics analysis and identified several nucleotide attributes within the full-length nucleotide sequences of HCV subtypes 1a and 1b that correlated with treatment outcome. Feature selection algorithms identified several nucleotide features (e.g. count of hydrogen and CG). Combination of algorithms utilized the selected nucleotide attributes and predicted HCV subtypes 1a and 1b therapy responders from non-responders with an accuracy of 75.00% and 85.00%, respectively. In addition, therapy responders and relapsers were categorized with an accuracy of 82.50% and 84.17%, respectively. Based on the identified attributes, decision trees were induced to differentiate different therapy response groups.ConclusionsThe present study identified new genetic markers that potentially impact the outcome of hepatitis C treatment. In addition, the results suggest new viral genomic attributes that might influence the outcome of IFN-mediated immune response to HCV infection.Electronic supplementary materialThe online version of this article (doi:10.1186/1756-0500-7-565) contains supplementary material, which is available to authorized users.

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Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

Cited by 8 publications

References 31 publications

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

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