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Prediction of matrilineal specific patatin-like protein governing in-vivo maternal haploid induction in maize using support vector machine and di-peptide composition
The mutant matrilineal ( mtl ) gene encoding patatin-like phospholipase activity is involved in in-vivo maternal haploid induction in maize. Doubling...
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Support Vector Machines and Support Vector Regression
In this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we...
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Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin...
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Novel Ensemble Machine Learning Paradigms for the Prediction of Antioxidant Activity of Bryophyllum pinnatum (Lam.) Oken
For herbal and modern drug research, secondary metabolites produced by plants are a valuable source of pharmaceutically active compounds. Northern...
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Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides
BackgroundCancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased...
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Machine learning-based donor permission extraction from informed consent documents
BackgroundWith more clinical trials are offering optional participation in the collection of bio-specimens for biobanking comes the increasing...
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PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
BackgroundCircadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the...
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circTIS: A Weighted Degree String Kernel with Support Vector Machine Tool for Translation Initiation Sites Prediction in circRNA
Recent studies discovered that peptides generated from the translation of circRNAs participate in several biological processes, many related to human...
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Quantifying corn LAI using machine learning and UAV multispectral imaging
Predicting leaf area index (LAI) is essential for understanding crop growth status and water-fertilizer management. The aim of this study was to...
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Machine learning: an advancement in biochemical engineering
One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has...
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FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation
AbstractDue to the high cost of DNA-binding proteins (DBPs) detection, many machine learning algorithms (ML) have been utilized to large-scale...
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Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and...
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Nondestructive and cost-effective silkworm, Bombyx mori (Lepidoptera: Bombycidae) cocoon sex classification using machine learning
Sericulture is the process of cultivating silkworm cocoons for the production of silks. The quality silk production requires quality seed production...
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Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm
AbstractPlant callus is generally considered to be a mass of undifferentiated cells and can be used for secondary metabolite production,...
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Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting
Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling...
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Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods
This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models...
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Can machine learning models provide accurate fertilizer recommendations?
Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates...
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Recent Advances in Applications of Support Vector Machines in Fungal Biology
Machine learning methods have been an especially useful and cost-effective way of predicting in fungal biology. Rapid identification of human fungal...
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Coupling continuous wavelet transform with machine learning to improve water status prediction in winter wheat
Water is one of the essential factors for crop growth and development. Rapid and non-destructive monitoring of winter wheat water status is crucial...
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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to...
