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Found 9 papers, 0 papers with code
AIS-MACA- Z: MACA based Clonal Classifier for Splicing Site, Protein Coding and Promoter Region Identification in Eukaryotes
This classifier can predict the splicing site with 97% accuracy.
Cellular Automata and Its Applications in Bioinformatics: A Review
This paper aims at providing a survey on the problems that can be easily addressed by cellular automata in bioinformatics.
AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for Protein Coding and Promoter Region Prediction
The proposed classifier is named as AIS-INMACA introduces a novel concept to combine CA with artificial immune system to produce a better classifier which can address major problems in bioinformatics.
An Extensive Repot on the Efficiency of AIS-INMACA (A Novel Integrated MACA based Clonal Classifier for Protein Coding and Promoter Region Prediction)
This paper exclusively reports the efficiency of AIS-INMACA.
Identification of Protein Coding Regions in Genomic DNA Using Unsupervised FMACA Based Pattern Classifier
But, the regions of these genes that code for proteins may occupy only a small region of the sequence.
HMACA: Towards Proposing a Cellular Automata Based Tool for Protein Coding, Promoter Region Identification and Protein Structure Prediction
This tool also predicts the structure of protein with an accuracy of 80%.
PSMACA: An Automated Protein Structure Prediction Using MACA (Multiple Attractor Cellular Automata)
Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes.
An Extensive Report on Cellular Automata Based Artificial Immune System for Strengthening Automated Protein Prediction
Artificial Immune System (AIS-MACA) a novel computational intelligence technique is can be used for strengthening the automated protein prediction system with more adaptability and incorporating more parallelism to the system.
Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics
CA has grown as potential classifier for addressing major problems in bioinformatics.
