Proteins are polypeptide chains carrying out most of the basic
functions of life at the molecular level. These linear chains fold in
complex 3D structures whose shape is responsible of proteins'
behavior. Thanks to several genome sequencing projects, the entire DNA sequence
of many organisms has been experimentally determined, and millions of
proteins sequences identified. However, physical methods, such as
NMR and crystallography, are not able to cope with this incredible number
of structures, and most of these proteins' 3D structures and functions
remain unknown. Therefore, it is becoming increasingly important to
predict protein's tertiary structure ab initio from its
amino acid sequence, using insights obtained from already known
Connectionist and probabilistic models for machine learning are often limited
to processing relatively poor data types. While some domains naturally
lead to static data representations, other domains are better described
by variable length representations. However, data in the real world often come with
a much richer structure and serial order can be generalized to more complex relations.
These relations can be naturally represented by graphs. While neural network and
probabilistic architectures have been extensively studied for both static and
sequential data types, architectures for data structures have received
relatively little attention until recently.
Kernel Methods for Machine Learning
Machine learning for the Web
Incremental processing in Natural Language
A Recursive Neural Networks based server for the prediction of secondary structures of proteins.
A server for the prediction of the bonding state and connectivity pattern of the cysteines in a given amino acid sequence.
A modified version of T. Joachims' SVM-Light that allows dynamic loading of plugins for arbitrary data types and kernels
SVM-Dlight plugin to work with kernel matrices
C++ source code for the Weighted Decompositional Kernel
C++ source code for the 3D Decompositional Kernel
Ph.D positions available at Machine Learning and Neural Networks Group
29th April 2003. Machine Learning and Neural Networks Group.
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