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A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete F...

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete F...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3154980646

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images

About this item

Full title

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images

Publisher

Cold Spring Harbor: Cold Spring Harbor Laboratory Press

Journal title

bioRxiv, 2025-01

Language

English

Formats

Publication information

Publisher

Cold Spring Harbor: Cold Spring Harbor Laboratory Press

More information

Scope and Contents

Contents

Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking t...

Alternative Titles

Full title

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_3154980646

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3154980646

Other Identifiers

E-ISSN

2692-8205

DOI

10.1101/2025.01.08.632028