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Protein Secondary Structure prediction using deep learning

It is a classification problem. In this study, machine learning models are developed for protein secondary structure prediction (PSSP), relative solvent accessibility prediction (RSA), and torsion angle prediction (TAP). PSSP aims to assign a secondary structure class to each amino acid of a protein. It can be predicted as 8-states or 3-states. In this work, the 8-state representation is transformed to 3-states. SA is the area that is accessible to solvent such as water and RSA is the SA normalized by the maximum accessible surface area. Similar to secondary structure, SA and RSA information is derived for each amino acid separately. It can be predicted as a real-valued quantity or it can be categorized and predicted as a discrete label.

Kĩ năng: Python, Deep Learning, Keras, Bioinformatics, Machine Learning (ML)

Về khách hàng:
( 0 nhận xét ) Gold Coast, Australia

ID dự án: #34835610

34 freelancer chào giá trung bình$1093 cho công việc này

umg536

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$1500 AUD trong 3 ngày
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6.5
Koki1216

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invincible1428

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ArkssTech

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adam7patrick

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josephvictory

Hi, I’ve gone through your Bioinformatics project briefing. I can assist you in building a deep learning classification predictive model that will accurately predict Protein Secondary Structures as 8-states or 3-states Thêm

$750 AUD trong 5 ngày
(3 Nhận xét)
3.9
aymanlafaz0

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ebaninajar

Hi This project can be implemented using the Keras framework with the Tensorflow backend. Here is my initial solution : 1- Use the whole protein sequence (primary structure) as an example for the CNN, with an output o Thêm

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(8 Nhận xét)
4.0