Prediction of Peptide and Protein Propensity for Amyloid Formation

Open Access

Peer-reviewed

Research Article

Carlos Família,

Affiliations School of Forensic and Investigative Science, University of Central Lancashire, Preston, PR1 2HE, United Kingdom, Centro de Investigação Interdisciplinar Egas Moniz, Instituto Superior de Ciências da Saúde Egas Moniz, Campus Universitário, Quinta, Da Granja, Monte de Caparica, 2829–511, Caparica, Portugal
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Sarah R. Dennison,

Affiliations Research and Innovation Office, UCLan Biomedical Research Facility, University of Central Lancashire, Preston, PR1 2HE, United Kingdom, School of Applied Science, London South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom
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Alexandre Quintas,

Affiliation Centro de Investigação Interdisciplinar Egas Moniz, Instituto Superior de Ciências da Saúde Egas Moniz, Campus Universitário, Quinta, Da Granja, Monte de Caparica, 2829–511, Caparica, Portugal
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David A. Phoenix

* E-mail: phoenixd@lsbu.ac.uk

Affiliation School of Applied Science, London South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom
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Neural networks
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Amyloid proteins
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Protein sequencing
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Artificial neural networks
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Polypeptides
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Forecasting
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Urea
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Protein structure
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