EuDiC SVM: A novel support vector machine classification algorithm

Bhavsar, Hetal; Ganatra, Amit

doi:10.3233/ida-150348

Cited by 11 publications

(19 citation statements)

References 21 publications

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“…IBk and K* are simple MLTs that work well on basic recognition problems. One of the shortcomings of the these two algorithms is that they are lazy learners, that is, they do not learn anything from the training data but simply use the training data itself for prediction because of which they are not robust for predicting noisy data . Due to this reason SVM and RF performed better than these MLTs in our study.…”

Section: Discussionmentioning

confidence: 83%

AVP‐IC₅₀Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC₅₀)

2015

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Peptide-based antiviral therapeutics has gradually paved their way into mainstream drug discovery research. Experimental determination of peptides' antiviral activity as expressed by their IC50 values involves a lot of effort. Therefore, we have developed "AVP-IC50 Pred," a regression-based algorithm to predict the antiviral activity in terms of IC50 values (μM). A total of 759 non-redundant peptides from AVPdb and HIPdb were divided into a training/test set having 683 peptides (T(683)) and a validation set with 76 independent peptides (V(76)) for evaluation. We utilized important peptide sequence features like amino-acid compositions, binary profile of N8-C8 residues, physicochemical properties and their hybrids. Four different machine learning techniques (MLTs) namely Support vector machine, Random Forest, Instance-based classifier, and K-Star were employed. During 10-fold cross validation, we achieved maximum Pearson correlation coefficients (PCCs) of 0.66, 0.64, 0.56, 0.55, respectively, for the above MLTs using the best combination of feature sets. All the predictive models also performed well on the independent validation dataset and achieved maximum PCCs of 0.74, 0.68, 0.59, 0.57, respectively, on the best combination of feature sets. The AVP-IC50 Pred web server is anticipated to assist the researchers working on antiviral therapeutics by enabling them to computationally screen many compounds and focus experimental validation on the most promising set of peptides, thus reducing cost and time efforts. The server is available at http://crdd.osdd.net/servers/ic50avp.

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Section: Discussionmentioning

confidence: 83%

AVP‐IC₅₀Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC₅₀)

2015

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“…The Support Vector Machine (SVM) represents a supervised machine learning paradigm that endeavors to identify a hyperplane effectively demarcating two classes [33]. Given that there exists an infinite number of hyperplanes capable of precisely segregating the two classes, SVM aims to select the optimal hyperplane by identifying the one with the maximum margin.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

Sotirov,

Dimitrov

2024

Applied Sciences

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The identification and characterization of immunogenic tumor antigens are essential for cancer vaccine development. In light of the impracticality of isolating and evaluating each putative antigen individually, in silico prediction algorithms, particularly those utilizing machine learning (ML) approaches, play a pivotal role. These algorithms significantly reduce the experimental workload necessary for discovering vaccine candidates. In this study, we employed six supervised ML methods on a dataset comprising 212 experimentally validated human tumor peptide antigens and an equal number of non-antigenic human peptides to develop models for immunogenicity prediction. These methods encompassed k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The models underwent validation through internal cross-validation within 10 groups from the training set and were further assessed using an external test set. Remarkably, the kNN model demonstrated superior performance, recognizing 90% of the known immunogens in the test set. The RF model excelled in the identification of non-immunogens, accurately classifying 93% of them in the test set. The three top-performing ML models according to multiple evaluation metrics (SVM, RF, and XGBoost) are to be subsequently integrated into the new version of the VaxiJen server, facilitating tumor antigen prediction through a majority voting mechanism.

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“…SVCs are used for separating a dataset into classes, producing a discrete output (class label). SVCs are widely used for both linear and non-linear data and are considered easily applicable to unseen datasets [105]. Although SVMs have some limitations, such as the inability to identify more than two classes at a time, or the difficulty of applying them to large datasets [106,107], they are still some of the most widely used classification algorithms in a few fields such as image classification and object detection in the field of remote sensing [107,108].…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…The support vectors thus define the distance (also called a "margin") between the two additional hyperplanes. Then, the SVM algorithm selects the classification hyperplane with an orientation that maximizes the margin, ensuring a more accurate classification [102,105,106,110].…”

Section: Support Vector Machinesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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EuDiC SVM: A novel support vector machine classification algorithm

Cited by 11 publications

References 21 publications

AVP‐IC₅₀Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC₅₀)

AVP‐IC₅₀Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC₅₀)

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

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EuDiC SVM: A novel support vector machine classification algorithm

Cited by 11 publications

References 21 publications

AVP‐IC50Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

AVP‐IC50Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

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Product

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AVP‐IC₅₀Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC₅₀)

AVP‐IC₅₀Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC₅₀)