Search Results - "Orhobor, Oghenejokpeme I."

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat by Grinberg, Nastasiya F., Orhobor, Oghenejokpeme I., King, Ross D.

“…In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called…”
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A simple spatial extension to the extended connectivity interaction features for binding affinity prediction by Orhobor, Oghenejokpeme I, Rehim, Abbi Abdel, Lou, Hang, Ni, Hao, King, Ross D

“…The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity…”
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Batched Bayesian Optimization for Drug Design in Noisy Environments by Bellamy, Hugo, Rehim, Abbi Abdel, Orhobor, Oghenejokpeme I., King, Ross

“…The early stages of the drug design process involve identifying compounds with suitable bioactivities via noisy assays. As databases of possible drugs are…”
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Transformational machine learning: Learning how to learn from many related scientific problems by Olier, Ivan, Orhobor, Oghenejokpeme I., Dash, Tirtharaj, Davis, Andy M., Soldatova, Larisa N., Vanschoren, Joaquin, King, Ross D.

“…Almost all machine learning (ML) is based on representing examples using intrinsic features. When there are multiple related ML problems (tasks), it is…”
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Cross-validation is safe to use by King, Ross D., Orhobor, Oghenejokpeme I., Taylor, Charles C.

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Imbalanced regression using regressor-classifier ensembles by Orhobor, Oghenejokpeme I., Grinberg, Nastasiya F., Soldatova, Larisa N., King, Ross D.

“…We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which…”
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Predicting rice phenotypes with meta and multi-target learning by Orhobor, Oghenejokpeme I., Alexandrov, Nickolai N., King, Ross D.

“…The features in some machine learning datasets can naturally be divided into groups. This is the case with genomic data, where features can be grouped by…”
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Protein–ligand binding affinity prediction exploiting sequence constituent homology by Abdel-Rehim, Abbi, Orhobor, Oghenejokpeme, Hang, Lou, Ni, Hao, King, Ross D

“…Abstract Motivation Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning…”
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A General Framework for Building Accurate and Understandable Genomic Models: A Study in Rice (Oryza sativa) by Orhobor, Oghenejokpeme I

“…Rapid technological advances in genotyping and sequencing technologies are driving the generation of vast amounts of genomic data. These advancements present a…”
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Transformative Machine Learning by Olier, Ivan, Orhobor, Oghenejokpeme I, Vanschoren, Joaquin, King, Ross D

“…The key to success in machine learning (ML) is the use of effective data representations. Traditionally, data representations were hand-crafted. Recently it…”
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