MOTIVATION Methods that focus on secondary structures, such as Position Specific Scoring Matrices and Hidden Markov Models, have proved useful for assigning proteins to families. However, for assigning proteins to an attribute class within a family these methods may introduce more free parameters than are needed. There are fewer members and there is less variability among sequences within a fam...

To help characterize the diversity in biological function of proteins emerging from the analysis of whole genomes, we present an operational definition of biological function that provides an explicit link between the functional classification of proteins and the effects of genetic variation or mutation on protein function. Using phylogenetic information, we establish definite criteria for func...

We used Artificial Neural Network for protein loop classification based on amino acid sequence alone. A new algorithm recently proposed, the Hidden Layer Learning Vector Quantization (HLVQ) was used and its accuracy compared with traditional Multilayer Preceptrons (MLP). The HLVQ algorithm achieved superior accuracy correctly classifying most loops.

The three-dimensional structure of a protein provides crucial information for predicting its function. However, as it is still a far more difficult and costly task to measure 3D coordinates of atoms in a protein than to sequence its amino acid composition, often we do not know the 3D structures of all the proteins at hand. Let us consider a kernel matrix that consists of kernel values represent...

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successi...

Protein classification can be performed by representing 3-D protein structures by graphs and then classifying the corresponding graphs. One effective way to classify such graphs is to use frequent subgraph patterns as features; however, the effectiveness of using subgraph patterns in graph classification is often hampered by the large search space of subgraph patterns. In this paper, the author...

The Eighth Joint Expert Committee on Nutrition of FAO and WHO1 emphasized the need for an accepted classification and definition of protein-calorie malnutrition. There are two pressing reasons for this. Bengoa' summarzed the available information about the frequency of protein-calorie malnutrition in different countries. There are many gaps, partly because for some countries there are no data, ...

The overall aim of this project is to investigate the use of Bayesian networks (Needham et al., 2006b) in integrating information, expressing relationships and making inferences or predictions on biological problems, motivated by data generation in genomics and proteomics. We have already successfully applied Bayesian networks to two problems in which we have previous experience. In the first i...

MOTIVATION Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine learning approaches provide good performance, but simplicity and computational efficiency of training and prediction are also important concerns. RESULTS We introduce a class of string kernels, calle...

Proteins were classified into their families using a classification tree method which is based on the coefficient of variations of physico-chemical and geometrical properties of the secondary structures of proteins. The tree method uses as splitting criterion the increase in purity when a node is split into two subnodes and the size of the tree is controlled by a threshold level for the improve...