Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction
نویسندگان
چکیده
منابع مشابه
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...
متن کاملProtein 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.
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RNA secondary structure prediction with pseudoknots is important, since pseudoknots are part of functionally improtant RNAs in cells. State of the art dynamic programming algorithms due to Akutsu et al [7] and Deogun et al [8] perform well on single RNA sequences. Our aim of this project is to be able to predict secondary structure of real life RNA sequences, which can be more than 700 nucleoti...
متن کاملPrediction 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...
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For certain categories of sequences, information from both the past and the future can be used for analysis and predictions at time t. This is the case for biological sequences where the nature and function of a region in a sequence may strongly depend on events located both upstream and downstream. We develop a new family of adaptive graphical model architectures for learning non-causal sequen...
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ژورنال
عنوان ژورنال: Journal of Molecular Modeling
سال: 2012
ISSN: 1610-2940,0948-5023
DOI: 10.1007/s00894-012-1410-7