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Protein inter-domain linker prediction using Random Forest and amino acid physiochemical properties.

Shatnawi M, Zaki N, Yoo PD - BMC Bioinformatics (2014)

Bottom Line: Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units.Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate prediction of protein domain linkers and boundaries is often regarded as the initial step of protein tertiary structure and function predictions. Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units. In this study, we propose a novel and accurate domain-linker prediction approach based on protein primary structure information only. We utilize a nature-inspired machine-learning model called Random Forest along with a novel domain-linker profile that contains physiochemical and domain-linker information of amino acid sequences.

Results: The proposed approach was tested on two well-known benchmark protein datasets and achieved 68% sensitivity and 99% precision, which is better than any existing protein domain-linker predictor. Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.

Conclusion: Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

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Number of generated trees optimization. Recall, precision, and F-measure at different number of generated trees performed on DS-All dataset.
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Figure 5: Number of generated trees optimization. Recall, precision, and F-measure at different number of generated trees performed on DS-All dataset.

Mentions: We set the number of selected features at each node for building the trees, m, to (log2(number of attributes) + 1) as recommended by [40]. We examined several values for the number of generated decision trees, Ntrees, in the range of 10 and 500 and found that the prediction accuracy increases as Ntrees increases as shown in Figure 5. However, the improvement in prediction when Ntrees exceeds 200 is not considerable when compared with the increase in computational time and memory. Therefore, we set Ntrees to 200 in all the conducted experiments. This also agrees with recent empirical studies [58,59] which reported that ensembles of size less or equal to 100 are too small for approximating the infinite ensemble prediction.


Protein inter-domain linker prediction using Random Forest and amino acid physiochemical properties.

Shatnawi M, Zaki N, Yoo PD - BMC Bioinformatics (2014)

Number of generated trees optimization. Recall, precision, and F-measure at different number of generated trees performed on DS-All dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4290662&req=5

Figure 5: Number of generated trees optimization. Recall, precision, and F-measure at different number of generated trees performed on DS-All dataset.
Mentions: We set the number of selected features at each node for building the trees, m, to (log2(number of attributes) + 1) as recommended by [40]. We examined several values for the number of generated decision trees, Ntrees, in the range of 10 and 500 and found that the prediction accuracy increases as Ntrees increases as shown in Figure 5. However, the improvement in prediction when Ntrees exceeds 200 is not considerable when compared with the increase in computational time and memory. Therefore, we set Ntrees to 200 in all the conducted experiments. This also agrees with recent empirical studies [58,59] which reported that ensembles of size less or equal to 100 are too small for approximating the infinite ensemble prediction.

Bottom Line: Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units.Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate prediction of protein domain linkers and boundaries is often regarded as the initial step of protein tertiary structure and function predictions. Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units. In this study, we propose a novel and accurate domain-linker prediction approach based on protein primary structure information only. We utilize a nature-inspired machine-learning model called Random Forest along with a novel domain-linker profile that contains physiochemical and domain-linker information of amino acid sequences.

Results: The proposed approach was tested on two well-known benchmark protein datasets and achieved 68% sensitivity and 99% precision, which is better than any existing protein domain-linker predictor. Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.

Conclusion: Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

Show MeSH