تقرير
Using recurrent neural networks to predict aspects of 3-D structure of folded copolymer sequences
| العنوان: | Using recurrent neural networks to predict aspects of 3-D structure of folded copolymer sequences |
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| المؤلفون: | Reilly, R. G., Kechadi, M. -T., Kuznetsov, Yu. A., Timoshenko, E. G., Dawson, K. A. |
| المصدر: | Il Nuovo Cimento D, 20 (12bis), pp. 2565-2574 (1998).ISSN 0392-6737 |
| سنة النشر: | 2024 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Soft Condensed Matter, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Physics - Computational Physics |
| الوصف: | The neural network techniques are developed for artificial sequences based on approximate models of proteins. We only encode the hydrophobicity of the amino acid side chains without attempting to model the secondary structure. We use our approach to obtain a large set of sequences with known 3-D structures for training the neural network. By employing recurrent neural networks we describe a way to augment a neural network to deal with sequences of realistic length and long-distant interactions between the sequence regions. Comment: 10 pages, 4 postscript figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.11493 |
| رقم الأكسشن: | edsarx.2407.11493 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |