PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

Protein secondary structure prediction.

The past year has seen a consolidation of protein secondary structure prediction methods. The advantages of prediction from an aligned family of proteins have been highlighted by several accurate predictions made 'blind', before any X-ray or NMR structure was known for the family. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.

Nonlinear Dimensional Reduction in Protein Secondary Structure Prediction

Prediction of Protein Secondary Structure Using Nonlinear Method

This paper presents the use of neural networks for the prediction of protein Secondary Structure. We propose a pre-processing stage based on the method of Cascaded Nonlinear Components Analysis (CNLPCA), in order to get a dimensional reduction of the data which may consider its nonlinearity. Then, the reduced data are placed in predictor networks and its results are combined. For the verificati...

Prediction of Protein Secondary Structure

In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure will be presented as a general guide to protein secondary structure prediction...

Protein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches

DNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functio...