Chapter title |
Protein secondary structure prediction.
|
---|---|
Chapter number | 19 |
Book title |
Data Mining Techniques for the Life Sciences
|
Published in |
Methods in molecular biology, March 2010
|
DOI | 10.1007/978-1-60327-241-4_19 |
Pubmed ID | |
Book ISBNs |
978-1-60327-240-7, 978-1-60327-241-4
|
Authors |
Pirovano W, Heringa J, Walter Pirovano, Jaap Heringa, Pirovano, Walter, Heringa, Jaap |
Abstract |
While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 1% |
Uruguay | 2 | <1% |
Germany | 2 | <1% |
France | 2 | <1% |
Poland | 2 | <1% |
United Kingdom | 2 | <1% |
India | 2 | <1% |
Colombia | 1 | <1% |
Australia | 1 | <1% |
Other | 6 | 2% |
Unknown | 237 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 68 | 26% |
Researcher | 40 | 15% |
Student > Master | 36 | 14% |
Student > Bachelor | 28 | 11% |
Student > Doctoral Student | 11 | 4% |
Other | 40 | 15% |
Unknown | 37 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 106 | 41% |
Biochemistry, Genetics and Molecular Biology | 42 | 16% |
Computer Science | 28 | 11% |
Chemistry | 11 | 4% |
Medicine and Dentistry | 8 | 3% |
Other | 21 | 8% |
Unknown | 44 | 17% |