machine learning methods (OChem Inc)
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OChem Inc
machine learning methods
Machine Learning Methods, supplied by OChem Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/ensemble+machine+learning+method/machine+learning+methods/pmc04937869-129-80-80
Average 90 stars, based on 1 article reviews
Machine Learning Methods, supplied by OChem Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/ensemble+machine+learning+method/machine+learning+methods/pmc04937869-129-80-80
Average 90 stars, based on 1 article reviews
machine learning methods - by Bioz Stars,
2026-09
90/100 stars
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Plasmid Preparation:Article Title: Computational prediction models for assessing endocrine disrupting potential of chemicals. Article Snippet: Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function.. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects.. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Derivative Assay:Article Title: Computational prediction models for assessing endocrine disrupting potential of chemicals. Article Snippet: Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function.. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects.. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. other:Article Title: In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods. Article Snippet: Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens.. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity.. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. Article Title: CERAPP: Collaborative Estrogen Receptor Activity Prediction Project Article Snippet: Models were developed using both well-known and innovative methods including partial least-squares (PLS) ( ; ), partial least-squares discriminant analysis (PLS-DA) ( ; ), decision forest (DF) ( , ; ; ), three-dimensional (3D) quantitative spectral data–activity relationship (QSDAR) ( ; ; ), support vector machines (SVM) , k nearest neighbors (kNN) ( ; ), associative artificial neural networks (ASNN) ( , ), PASS algorithm derived from Naïve Bayes classifier , self-consistent regression with radial basis function interpolation (RBF-SCR) , |