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T4Seeker: a hybrid model for type IV secretion effectors identification
by
Li, Jing
, Ni, Fengming
, Zou, Quan
, He, Shida
, Zhang, Jian
, Zhang, Feng
in
Amino acids
/ Bacteria
/ Biomedical and Life Sciences
/ Business metrics
/ Cell interactions
/ Coliforms
/ Disease
/ E coli
/ Effectors
/ Evolutionary scale modeling
/ Feature fusion
/ Gene expression
/ Identification
/ Laboratories
/ Large language models
/ Life Sciences
/ Long short-term memory
/ Machine learning
/ Predictions
/ Proteins
/ Secretion
/ Type IV secretion effectors
2024
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T4Seeker: a hybrid model for type IV secretion effectors identification
by
Li, Jing
, Ni, Fengming
, Zou, Quan
, He, Shida
, Zhang, Jian
, Zhang, Feng
in
Amino acids
/ Bacteria
/ Biomedical and Life Sciences
/ Business metrics
/ Cell interactions
/ Coliforms
/ Disease
/ E coli
/ Effectors
/ Evolutionary scale modeling
/ Feature fusion
/ Gene expression
/ Identification
/ Laboratories
/ Large language models
/ Life Sciences
/ Long short-term memory
/ Machine learning
/ Predictions
/ Proteins
/ Secretion
/ Type IV secretion effectors
2024
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Do you wish to request the book?
T4Seeker: a hybrid model for type IV secretion effectors identification
by
Li, Jing
, Ni, Fengming
, Zou, Quan
, He, Shida
, Zhang, Jian
, Zhang, Feng
in
Amino acids
/ Bacteria
/ Biomedical and Life Sciences
/ Business metrics
/ Cell interactions
/ Coliforms
/ Disease
/ E coli
/ Effectors
/ Evolutionary scale modeling
/ Feature fusion
/ Gene expression
/ Identification
/ Laboratories
/ Large language models
/ Life Sciences
/ Long short-term memory
/ Machine learning
/ Predictions
/ Proteins
/ Secretion
/ Type IV secretion effectors
2024
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T4Seeker: a hybrid model for type IV secretion effectors identification
Journal Article
T4Seeker: a hybrid model for type IV secretion effectors identification
2024
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Overview
Background
The type IV secretion system is widely present in various bacteria, such as
Salmonella
,
Escherichia coli
, and
Helicobacter pylori
. These bacteria use the type IV secretion system to secrete type IV secretion effectors, infect host cells, and disrupt or modulate the communication pathways. In this study, type III and type VI secretion effectors were used as negative samples to train a robust model.
Results
The area under the curve of T4Seeker on the validation and independent test sets were 0.947 and 0.970, respectively, demonstrating the strong predictive capacity and robustness of T4Seeker. After comparing with the classic and state-of-the-art T4SE identification models, we found that T4Seeker, which is based on traditional features and large language model features, had a higher predictive ability.
Conclusion
The T4Seeker proposed in this study demonstrates superior performance in the field of T4SEs prediction. By integrating features at multiple levels, it achieves higher predictive accuracy and strong generalization capability, providing an effective tool for future T4SE research.
Publisher
BioMed Central,Springer Nature B.V,BMC
Subject
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