PRMACA: A Promoter Region Identification Using Multiple Attractor Cellular Automata (MACA)

Sree, Pokkuluri Kiran; Babu, Inampudi Ramesh; Nedunuri, Srinivas

doi:10.1007/978-3-319-03107-1_42

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The performance of DL-16-MACA is compared with GOR [9], PSIPRED [10], PHD [11], JPred [12] and R5NCA [13] as shown in table2 and figure .GOR reports very less prediction accuracy of 65%, as it depends only on amino acid frequency . The learning mechanism employed in DL-16-MACA predicts Helix class with high accuracy, but is the not the case in Stands and Coiled classes The lesser accuracy in prediction is due to the poor mapping of physical parameters with the CA parameters .…”

Section: Figure 1 Sensitivity Precision Calculation For the Three CLmentioning

confidence: 99%

Deep Learning Mechanism Augmented with 16-Hybrid Cellular Automata For Secondary Structure Prediction

Sreee*

2019

IJITEE

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A protein plays various role in our human body like cellular development, reproduction, endurance and regulation of human body. Based on the structure of the genes we can extract lots of information regarding the human body. It is very easy to extract lots of information from a structure than a sequence. Identifying the protein structure helps in drug design. The secondary structure, to some extent tells about the effect of amino acid changes and explains the reason for the disease of an individual. A doctor can suggest medicines without any side effects to a patient based on the protein structure acquired from DNA. We have developed a classifier DL-16-MACA which can predict the secondary structure of an amino acid sequence of different lengths. In this prediction we have considered three classes Helix (H), Strands (E), Coiled(C). For Helix class the sensitivity, percentage accuracy is 0.923 and 90.6% respectively. For Strands class the sensitivity, percentage accuracy is 0.852 and 85.55%respectively. For Coiled class the sensitivity, percentage accuracy is 0.789 and 77.1% respectively. The percentage accuracy when tested with PDB datasets is 85.4% which substantially comparable with existing literature.

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Section: Figure 1 Sensitivity Precision Calculation For the Three CLmentioning

confidence: 99%

Deep Learning Mechanism Augmented with 16-Hybrid Cellular Automata For Secondary Structure Prediction

Sreee*

2019

IJITEE

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A Human Promoter Prediction using MACA-CLONAL Classifier

Sree¹,

Babu²

2014

Enliven: Bioinform

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PRMACA: A Promoter Region Identification Using Multiple Attractor Cellular Automata (MACA)

Cited by 2 publications

References 13 publications

Deep Learning Mechanism Augmented with 16-Hybrid Cellular Automata For Secondary Structure Prediction

Deep Learning Mechanism Augmented with 16-Hybrid Cellular Automata For Secondary Structure Prediction

A Human Promoter Prediction using MACA-CLONAL Classifier

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