Difference between revisions of "Victor"

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Proteins are particularly suited to machine learning (ML) due to the wealth of available sequence and structural information. This data availability is a result of the recent advances in next generation sequencing technologies and in vitro determination of structures deposited in the Protein Data Bank (PDB). Moreover, nature conserves the same structures and functions thus allowing pattern matching ML approaches to excel. However, representing the proteins is a tricky issue, extracting the relevant data complicated by the protein representations. Thus the first stage of any protein ML approach is complicated by the need to software engineer the data extraction (e.g. extracting residue-residue contacts from a PDB structure). Our lab has recently developed VICTOR which is an easy to use C++ library for extracting relevant protein features. We show that with a simple wrapper VICTOR can be easily incorporated into the ML package WEKA, opening a rich set of ML algorithms to the world of proteins. An interesting example is also shown by clustering the Torsion Angle Potentials (TAPs) of 40,000 protein structures.  
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Proteins are particularly suited to machine learning (ML) due to the wealth of available sequence and structural information. This data availability is a result of the recent advances in next generation sequencing technologies and in vitro determination of structures deposited in the Protein Data Bank (PDB). Moreover, nature conserves the same structures and functions thus allowing pattern matching ML approaches to spreadsheet software. However, representing the proteins is a tricky issue, extracting the relevant data became complicated by the protein representations. Thus the first stage of any protein ML approach is complicated by the need to software engineer the data extraction (e.g. extracting residue-residue contacts from a PDB structure). Our lab has recently developed VICTOR which is an easy to use C++ library for extracting relevant protein features. We show that with a simple wrapper VICTOR can be easily incorporated into the ML package WEKA, opening a rich set of ML algorithms to the world of proteins. An interesting example is also shown by clustering the Torsion Angle Potentials (TAPs) of 40,000 protein structures.  
 
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Victor is composed of three main modules:  
 
Victor is composed of three main modules:  
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This Wiki will help you to discover how to use the Victor package through an example driven approach. We believe this is the easiest way to get confident with the Victor library.  
 
This Wiki will help you to discover how to use the Victor package through an example driven approach. We believe this is the easiest way to get confident with the Victor library.  
For a detailed description of all classes please visit the '''Doxygen''' documentation [http://kim.bio.unipd.it/local/DoxyBiopool2014/index.html  Victor2.0 complete guide].
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For a detailed description of all classes and methods please visit the '''Doxygen''' documentation [http://kim.bio.unipd.it/local/DoxyBiopool2014/index.html  Victor2.0 complete guide].
 
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[[Getting started]] Here you will find a resume guide on how to download and install Victor2.0.
 
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Revision as of 08:37, 8 July 2014

The Victor2.0 library (Virtual Construction Toolkit for Proteins) is an open-source project dedicated to providing a C++ implementation of tools for analyzing and manipulating protein structures.

Proteins are particularly suited to machine learning (ML) due to the wealth of available sequence and structural information. This data availability is a result of the recent advances in next generation sequencing technologies and in vitro determination of structures deposited in the Protein Data Bank (PDB). Moreover, nature conserves the same structures and functions thus allowing pattern matching ML approaches to spreadsheet software. However, representing the proteins is a tricky issue, extracting the relevant data became complicated by the protein representations. Thus the first stage of any protein ML approach is complicated by the need to software engineer the data extraction (e.g. extracting residue-residue contacts from a PDB structure). Our lab has recently developed VICTOR which is an easy to use C++ library for extracting relevant protein features. We show that with a simple wrapper VICTOR can be easily incorporated into the ML package WEKA, opening a rich set of ML algorithms to the world of proteins. An interesting example is also shown by clustering the Torsion Angle Potentials (TAPs) of 40,000 protein structures. 

Victor is composed of three main modules:

  • Biopool - Biopolymer Object Oriented Library. The core library that generates the protein object and provides useful methods to manipulate the structure.
  • Energy - Energy functions implementation.
  • Lobo - LOop Build-up and Optimization.

This Wiki will help you to discover how to use the Victor package through an example driven approach. We believe this is the easiest way to get confident with the Victor library. For a detailed description of all classes and methods please visit the Doxygen documentation Victor2.0 complete guide.

Getting started Here you will find a resume guide on how to download and install Victor2.0.

Template:Caja 1

Getting started Here you will find a resume guide on how to download and install Victor2.0.

Detailed Installation Here you will find a complete guide on how to download and install Victor2.0.

Introduction to Victor package Here you will find an explanation for some of the concepts used for victor2.0 package.

Tutorial how to use the Victor package through an example driven approach

Community Here you will find information about the research lab Biocomputing Up.

Template:Developers Here you will find the complete documentation for the source code. Detailed documentation for all classes and methods.

Template:Applications Here you will find a list of the applications that use the package

Template:References Here you will find references for some parts of the package