Artificial Neural Network Model for Predicting Protein Subcellular Location
نویسندگان
چکیده
The function of a protein is closely correlated to its subcellular location. Is it possible to utilize a bioinformatics method to predict the protein subcellular location? To explore this problem, proteins are classified into 12 groups (Protein Eng. 12 (1999) 107-118) according to their subcellular location: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole. In this paper, the neural network method was proposed to predict the subcellular location of a protein according to its amino acid composition. Results obtained through self-consistency, cross-validation and independent dataset tests are quite high. Accordingly, the present method can serve as a complement tool for the existing prediction methods in this area.
منابع مشابه
Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network
Suspended particles have deleterious effects on human health and one of the reasons why Tehran is effected is its geographically location of air pollution. One of the most important ways to reduce air pollution is to predict the concentration of pollutants. This paper proposed a hybrid method to predict the air pollution in Tehran based on particulate matter less than 10 microns (PM10), and the...
متن کاملEstimating scour below inverted siphon structures using stochastic and soft computing approaches
This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation of maximum scour depth, location of the scour hole, location and height of the dune downs...
متن کاملA System for Predicting Subcellular Localization of Yeast Genome Using Neural Network
The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. Many efforts have been made to predict protein subcellular localization. This paper...
متن کاملPredicting Force in Single Point Incremental Forming by Using Artificial Neural Network
In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and t...
متن کاملArtificial neural network models for production of nano-grained structure in AISI 304L stainless steel by predicting thermo-mechanical parameters
An artificial neural network (ANN) model is developed for the analysis, simulation, and prediction of the austenite reversion in the thermo-mechanical treatment of 304L austenitic stainless steel. The results of the ANN model are in good agreement with the experimental data. The model is used to predict an appropriate annealing condition for austenite reversion through the martensite to austeni...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Computers & chemistry
دوره 26 2 شماره
صفحات -
تاریخ انتشار 2002