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Mendeley Ltd machine learning modeling code
Machine Learning Modeling Code, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/model+code/learning+machine/pmc12804608-199-2-14
Average 86 stars, based on 1 article reviews
machine learning modeling code - by Bioz Stars, 2026-10
86/100 stars

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Related Articles

Control:

Article Title: Investor sentiment spillover from air pollution: Cross-industry influences on stock markets
Article Snippet: This study employs the LASSO machine learning technique for robust causal inference.. While air pollution is primarily linked to heavily polluting industries, we identify a pervasive sentiment spillover channel that transmits shocks to seemingly unrelated sectors.. This occurs because pollution–induced negative sentiment alters investors’ risk appetite, prompting portfolio adjustments across various industries to express their green preferences.

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3 .

Article Title: Nomogram for anemia risk prediction and validation in sepsis patients
Article Snippet: • The machine learning modeling code used in this study has been deposited in Mendeley Data alongside the clinical data, with the same DOI accession code https://doi.org/10.17632/fp6xdds387.1 .

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3.

Biomarker Discovery:

Article Title: Investor sentiment spillover from air pollution: Cross-industry influences on stock markets
Article Snippet: This study employs the LASSO machine learning technique for robust causal inference.. While air pollution is primarily linked to heavily polluting industries, we identify a pervasive sentiment spillover channel that transmits shocks to seemingly unrelated sectors.. This occurs because pollution–induced negative sentiment alters investors’ risk appetite, prompting portfolio adjustments across various industries to express their green preferences.

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3 .

Article Title: Nomogram for anemia risk prediction and validation in sepsis patients
Article Snippet: • The machine learning modeling code used in this study has been deposited in Mendeley Data alongside the clinical data, with the same DOI accession code https://doi.org/10.17632/fp6xdds387.1 .

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3.

Activation Assay:

Article Title: Investor sentiment spillover from air pollution: Cross-industry influences on stock markets
Article Snippet: This study employs the LASSO machine learning technique for robust causal inference.. While air pollution is primarily linked to heavily polluting industries, we identify a pervasive sentiment spillover channel that transmits shocks to seemingly unrelated sectors.. This occurs because pollution–induced negative sentiment alters investors’ risk appetite, prompting portfolio adjustments across various industries to express their green preferences.

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3 .

Article Title: Nomogram for anemia risk prediction and validation in sepsis patients
Article Snippet: • The machine learning modeling code used in this study has been deposited in Mendeley Data alongside the clinical data, with the same DOI accession code https://doi.org/10.17632/fp6xdds387.1 .

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3.

Selection:

Article Title: Investor sentiment spillover from air pollution: Cross-industry influences on stock markets
Article Snippet: This study employs the LASSO machine learning technique for robust causal inference.. While air pollution is primarily linked to heavily polluting industries, we identify a pervasive sentiment spillover channel that transmits shocks to seemingly unrelated sectors.. This occurs because pollution–induced negative sentiment alters investors’ risk appetite, prompting portfolio adjustments across various industries to express their green preferences.

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3 .

Article Title: Nomogram for anemia risk prediction and validation in sepsis patients
Article Snippet: • The machine learning modeling code used in this study has been deposited in Mendeley Data alongside the clinical data, with the same DOI accession code https://doi.org/10.17632/fp6xdds387.1 .

Article Title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.
Article Snippet: Intermediate files and results of machine learning have been deposited at Mendeley under DOI: https://doi.org/10.17632/zs2mbjv7dn.3.



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