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machine learning frameworks (KNIME GmbH)

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KNIME GmbH machine learning frameworks
Machine Learning Frameworks, supplied by KNIME GmbH, 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/machine+learning/machine+learning+algorithms/pm38078415-26-2-10
Average 90 stars, based on 1 article reviews

machine learning frameworks - by Bioz Stars, 2026-09

90/100 stars

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Article Title: Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.
Article Snippet: Tsoni R, Kaldis V, Kapogianni I, Sakagianni A, Feretzakis G, Verykios VS. A machine learning pipeline using KNIME to predict hospital admission in the MIMIC-IV database.

Binding Assay:

Article Title: Prediction of Peptide and TCR CDR3 Loops in Formation of Class I MHC-Peptide-TCR Complexes Using Molecular Models with Solvation
Article Snippet: .. These parameters are used to develop a machine learning model in KNIME [ 12 ] for subsequent prediction of binding for MHC-peptide-TCR complexes. ..

Immunopeptidomics:

Functional Assay:

Article Title: Will the hype of automated drug discovery finally be realized?
Article Snippet: More importantly, KNIME also supports many programming languages including Python, R and Bash, which facilitate the users’ general tasks. .. Furthermore, most popular machine learning frameworks have been embedded in KNIME as well, although the functional modules for certain AI frameworks might be different from its original version. .. KNIME also provides API for many widely used databases such as PDB Bank, PubChem, BindingDB and others, which further facilitates data analysis in this platform.

Generated:

Article Title: Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.
Article Snippet: The drug combination is an attractive approach for cancer treatment.. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively.. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells.