Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information
Abstract
:1. Introduction
2. Methods
2.1. Local Fractal Dimension
2.2. Local Fuzzy Fractal Dimension
2.3. Subcellular Compartment Score
2.4. LDS Algorithm
3. Results and Discussion
3.1. Experimental Data
3.2. Performance of the LDS Algorithm
3.3. Influence of the Parameter α
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Datasets | Proteins | Interactions | Essential Proteins | Non-Essential Proteins |
---|---|---|---|---|
DIP4746 | 4746 | 15,166 | 1130 | 3616 |
DIP5093 | 5093 | 24,743 | 1167 | 3926 |
MIPS4546 | 4546 | 12,319 | 1016 | 3530 |
Datasets | Methods | SN | SP | PPV | NPV | F-Measure | ACC |
---|---|---|---|---|---|---|---|
DIP4746 | DC | 0.5469 | 0.7561 | 0.412 | 0.8423 | 0.470 | 0.7063 |
SC | 0.500 | 0.7414 | 0.3767 | 0.8259 | 0.4297 | 0.6839 | |
BC | 0.4681 | 0.7315 | 0.3527 | 0.8148 | 0.4023 | 0.6688 | |
CloseC | 0.4611 | 0.7293 | 0.3473 | 0.8124 | 0.3962 | 0.6654 | |
ClusterC | 0.5336 | 0.7519 | 0.402 | 0.8376 | 0.4586 | 0.700 | |
IC | 0.5478 | 0.7564 | 0.4127 | 0.8426 | 0.4707 | 0.7067 | |
LAC | 0.5451 | 0.7555 | 0.4107 | 0.8417 | 0.4684 | 0.7054 | |
PeC | 0.4717 | 0.7326 | 0.3553 | 0.8161 | 0.4053 | 0.6705 | |
LID | 0.554 | 0.7583 | 0.4173 | 0.8447 | 0.4760 | 0.7097 | |
LDS | 0.5673 | 0.7624 | 0.4273 | 0.8494 | 0.4875 | 0.716 | |
DIP5093 | DC | 0.4901 | 0.7636 | 0.3813 | 0.8344 | 0.4289 | 0.701 |
SC | 0.4559 | 0.7534 | 0.3547 | 0.8233 | 0.399 | 0.6853 | |
BC | 0.4165 | 0.7417 | 0.324 | 0.8105 | 0.3645 | 0.6672 | |
CloseC | 0.4422 | 0.7494 | 0.344 | 0.8188 | 0.387 | 0.679 | |
ClusterC | 0.4773 | 0.7598 | 0.3713 | 0.8302 | 0.4177 | 0.6951 | |
IC | 0.4876 | 0.7629 | 0.3793 | 0.8336 | 0.4267 | 0.6998 | |
LAC | 0.5193 | 0.7723 | 0.404 | 0.8439 | 0.4544 | 0.7143 | |
PeC | 0.4619 | 0.7552 | 0.3593 | 0.8252 | 0.4042 | 0.688 | |
LID | 0.5261 | 0.7743 | 0.4093 | 0.8461 | 0.4604 | 0.7175 | |
LDS | 0.5467 | 0.7804 | 0.4253 | 0.8528 | 0.4784 | 0.7269 | |
MIPS4546 | DC | 0.4242 | 0.6972 | 0.2873 | 0.8079 | 0.3426 | 0.6362 |
SC | 0.2776 | 0.655 | 0.188 | 0.759 | 0.2242 | 0.5706 | |
BC | 0.3917 | 0.6878 | 0.2653 | 0.7971 | 0.3164 | 0.6216 | |
CloseC | 0.2825 | 0.6564 | 0.1913 | 0.7607 | 0.2281 | 0.5728 | |
ClusterC | 0.4242 | 0.6972 | 0.2873 | 0.8079 | 0.3426 | 0.6361 | |
IC | 0.3858 | 0.6861 | 0.2613 | 0.7951 | 0.3116 | 0.619 | |
LAC | 0.4242 | 0.6972 | 0.2873 | 0.8079 | 0.3426 | 0.6362 | |
PeC | 0.4232 | 0.6969 | 0.2867 | 0.8076 | 0.3418 | 0.6357 | |
LID | 0.4311 | 0.6992 | 0.292 | 0.8102 | 0.3482 | 0.6392 | |
LDS | 0.5719 | 0.7397 | 0.3873 | 0.8572 | 0.4618 | 0.7022 |
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Shen, L.; Zhang, J.; Wang, F.; Liu, K. Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information. Genes 2022, 13, 173. https://doi.org/10.3390/genes13020173
Shen L, Zhang J, Wang F, Liu K. Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information. Genes. 2022; 13(2):173. https://doi.org/10.3390/genes13020173
Chicago/Turabian StyleShen, Li, Jian Zhang, Fang Wang, and Kai Liu. 2022. "Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information" Genes 13, no. 2: 173. https://doi.org/10.3390/genes13020173
APA StyleShen, L., Zhang, J., Wang, F., & Liu, K. (2022). Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information. Genes, 13(2), 173. https://doi.org/10.3390/genes13020173