Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs

• Published in: Scientific Reports Vol. 11; no. 1; pp. 24359 - 12
• Main Authors: Takamura, Ayari, Tsukamoto, Kaede, Sakata, Kenji, Kikuchi, Jun
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 21.12.2021; Nature Publishing Group UK; Nature Publishing Group; Nature Portfolio
• Subjects: 639/638/11/878/1264; 639/638/630; Algorithms; Animals; Biopolymers; Biopolymers - analysis; Cats; Cattle; Differential thermal analysis; Fourier analysis; Fourier transforms; Hair; Hair - chemistry; Humanities and Social Sciences; Humans; Infrared spectroscopy; Keratins; Keratins - analysis; Learning algorithms; Machine Learning; Magnetic Resonance Spectroscopy; Mechanical properties; Medicine; multidisciplinary; NMR; Nuclear magnetic resonance; Physical Phenomena; Physical properties; Polymers; Prediction models; Q; R; Science; Science (multidisciplinary); Spectroscopy, Fourier Transform Infrared; Swine; Thermal analysis

Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniq...