Prediction of amyloid aggregation rates by machine learning and feature selection

Yang, Wuyue; Tan, Pengzhen; Fu, Xinping; Hong, Liu

doi:10.1063/1.5113848

Cited by 13 publications

(5 citation statements)

References 66 publications

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“…For drug development, feature selection and classifiers are used to predict functional classes of newly generated protein sequences [ 25 ] and protein inhibitors and substrates [ 26 ]. In clinical tests, they are used to predict the rate of amyloid aggregation [ 27 ] and the production of high antivasoactive peptides [ 28 ]. In summary, the main idea of these studies is to narrow the feature set by filtering the relevant data through a combination of single or multiple feature algorithms [ 29 ] and then enter the new feature set into the classifier to classify and find the indicator that is most closely related to the disease.…”

Section: Related Workmentioning

confidence: 99%

Personalized Body Constitution Inquiry Based on Machine Learning

Fan

Wen

et al. 2020

Journal of Healthcare Engineering

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Background. Body constitution (BC) is the abstract concept indicating the state of a person’s health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient’s objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor’s diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods. This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results. The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions. The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient’s BC types.

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Section: Related Workmentioning

confidence: 99%

Personalized Body Constitution Inquiry Based on Machine Learning

Fan

Wen

et al. 2020

Journal of Healthcare Engineering

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“…The proteins anchored in the membrane can reveal the sensitivity of the polypeptide chain to its surroundings [ 6 ]. The aggregation of proteins can be treated as partial non-solubility, which is sometimes necessary in order to ensure biological activity, as well as loss of activity, as is observed in the misfolded proteins [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]—of which the amyloids are spectacular examples [ 19 ]. Recently, the intensively applied machine learning technique (applied in PROSO, for example) enables the prediction of protein solubility in heterologous expression in Escherica coli [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution

Ptak-Kaczor

Banach

Stąpor

et al. 2021

IJMS

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Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.

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“…Computational approaches have been developed to estimate mechanistic and kinetic characteristics for better comprehension and prediction of colloidal instability and aggregation. Mechanistic tools aid in screening and minimizing APRs during the discovery phase (Kuhn et al, 2017;Prabakaran et al, 2017Prabakaran et al, , 2020van der Kant et al, 2017;Gil-Garcia et al, 2018;Rawat et al, 2018;Bauer et al, 2020;Ebo et al, 2020;Shahfar et al, 2022), while kinetic predictors Frontiers in Molecular Biosciences frontiersin.org 13 estimate aggregation rates, crucial for liquid formulation development meeting regulatory requirements for shelf life (Rawat et al, 2019;Yang et al, 2019;Santos et al, 2020). Machine Learning (ML) can train kinetic models using extensive data sets with experimental and sequence/structure information (Rawat et al, 2019;Yang et al, 2019), facilitating prediction of optimal formulation compositions (pH, salt, excipients) for minimal kinetics.…”

Section: In Silico Assessments In Early Developmentmentioning

confidence: 99%

Progress and challenges in computational structure-based design and development of biologic drugs

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Prediction of amyloid aggregation rates by machine learning and feature selection

Cited by 13 publications

References 66 publications

Personalized Body Constitution Inquiry Based on Machine Learning

Personalized Body Constitution Inquiry Based on Machine Learning

Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution

Progress and challenges in computational structure-based design and development of biologic drugs

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