File Entry: Optimized Local Protein Structure with Support Vector Machine to Predict Protein Secondary Structure
Created: 2012-07-24 04:21:20
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| Title | Optimized Local Protein Structure with Support Vector Machine to Predict Protein Secondary Structure |
| File name | Chin Yin Fai.pdf |
| File size | 499682 |
| SHA1 | 0a8eeeae6a72f64444c2d9597261fdce88a6d6ea |
| Content type | Adobe PDF |
Description
Protein includes many substances, such as enzymes, hormones and
antibodies that are necessary for the organisms. Living cells are controlled by
proteins and genes that interact through complex molecular pathways to achieve
a specific function. These proteins have different shapes and structures which
distinct them from each other. By having unique structures, only proteins able
to carried out their function efficiently. Therefore, determination of protein
structure is fundamental for the understanding of the cell’s functions. The
function of a protein is also largely determined by its structure. The importance
of understanding protein structure has fueled the development of protein
structure databases and prediction tools. Computational methods which were
able to predict protein structure for the determination of protein function
efficiently and accurately are in high demand. In this study, local protein
structure with Support Vector Machine is proposed to predict protein secondary
structure.
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