Searching Protein 3-D Structures for Optimal Structure Alignment Using Intelligent Algorithms and Data Structures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F10%3A86075394" target="_blank" >RIV/61989100:27240/10:86075394 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Searching Protein 3-D Structures for Optimal Structure Alignment Using Intelligent Algorithms and Data Structures

Popis výsledku v původním jazyce

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the currentresearch collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins-all to all protein similarity. For the calculation of protein similarity, we use term frequency x inverse document frequency (tf x idf) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision.

Název v anglickém jazyce

Searching Protein 3-D Structures for Optimal Structure Alignment Using Intelligent Algorithms and Data Structures

Popis výsledku anglicky

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the currentresearch collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins-all to all protein similarity. For the calculation of protein similarity, we use term frequency x inverse document frequency (tf x idf) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE

ISSN

1089-7771

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

000283982200008

EID výsledku v databázi Scopus

—