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codon optimization » wolf optimization (Expand Search)
acid optimization » based optimization (Expand Search), lead optimization (Expand Search), art optimization (Expand Search)
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based acid » based bci (Expand Search), based ai (Expand Search), based agi (Expand Search)
codon optimization » wolf optimization (Expand Search)
acid optimization » based optimization (Expand Search), lead optimization (Expand Search), art optimization (Expand Search)
library based » laboratory based (Expand Search)
based acid » based bci (Expand Search), based ai (Expand Search), based agi (Expand Search)
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Diversity and specificity of lipid patterns in basal soil food web resources
Published 2019“…In marine environments, multivariate optimization models (Quantitative Fatty Acid Signature Analysis) and Bayesian approaches (source-tracking algorithm) were established to predict the proportion of predator diets using lipids as tracers. …”
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Optimal 8-mer and 9-mer SARS-CoV-2 epitope identification.
Published 2020“…Peptide sequences are named based on the protein they are contained within, followed by the number of the first amino acid residue of the peptide in the context of the full protein, to the last amino acid residue. potential optimal 8-mer or 9-mer CD8+ T cell epitopes were predicted. …”
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Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products
Published 2024“…The increasing ability to incorporate noncanonical amino acids and complement translation with recombinant enzymes has enabled cell-free production of peptide-based natural products (NPs) and NP-like molecules. …”
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Data_Sheet_1_CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage.docx
Published 2021“…In this study, we describe an enhanced gradient-based optimizer that includes the Cauchy and Levy mutation strategy (CLGBO) to construct DNA coding sets, which are used as primer and address libraries. …”
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<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043)
Published 2025“…The described extracted features were used to predict leaf betalain content (µg per FW) using multiple machine learning regression algorithms (Linear regression, Ridge regression, Gradient boosting, Decision tree, Random forest and Support vector machine) using the <i>Scikit-learn</i> 1.2.1 library in Python (v.3.10.1) (list of hyperparameters used is given in <a href="#sup1" target="_blank">Supplementary Data S5</a>). …”