Along the long path from genomic data to a new drug, the knowledge of three-dimensional protein structure can be of significant help in several places. This paper points out such places, discusses the virtues of protein structure knowledge and reviews bioinformatics methods for gaining such knowledge on the protein structure.

Several methodologies have been developed to identify genes and classify DNA sequences into coding and non-coding sequences. This classification process is fundamental in gene finding and gene annotation tools and is one of the most challenging tasks in bioinformatics and computational biology. The approach described herein measures mutual information (MIM) found in DNA sequences at the amino a...

Simplified amino acid alphabets have been successful in several areas of bioinformatics, including predicting protein structure, predicting protein function, and protein classification. Since the number of possible simplifications is large, it is not practical to search through all possible simplifications to find one suitable for a specific application. A previous study conducted by the author...

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem, where the protein functions to be predicted correspond to classes that are arranged in a hierarchical structure, in the form of a class tree. T...

We consider the problem of computing nearest neighbors in an arbitrary metric space, particularly a metric space that cannot be easily embedded in R. We present a data structure, the Partition Tree, that can be constructed in O(n logn) time, where n is the size of the set of points to be searched, and has been experimentally shown to have an average query time that is a sublinear function of n ...

Protein secondary structure prediction is one of major challenges in bioinformatics, data ming. In this paper, we propose a novel intelligent prediction system model-Compound Pyramid System Model, which may become the classic model for predicting protein secondary structure. It consists of four components by intelligent interfaces and synthesizing several methods such as SAC (Structural associa...

In this paper a new technique for the solutions of hard computational problems in bioinformatics is investigated. This is the $-calculus process algebra for problem solving that applies the cost performance measures to converge to optimal solutions with minimal problem solving costs. We demonstrate that the $-calculus generic search method, called the kΩ-optimization, can be used to solve gene ...

We present a probabilistic logic program to generate an educational puzzle that introduces the basic principles of next generation sequencing, gene finding and the translation of genes to proteins following the central dogma in biology. In the puzzle, a secret ”protein word” must be found by assembling DNA from fragments (reads), locating a gene in this sequence and translating the gene to a pr...

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Baye...

MicroRNAs (miRNAs) are key regulators of eukaryotic gene expression whose fundamental role has been already identified in many cell pathways. The correct identification of miRNAs targets is a major challenge in bioinformatics. So far, machine learning-based methods for miRNA-target prediction have shown the best results in terms of specificity and sensitivity. However, despite its well-known ef...