Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

Background: Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by...

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Bibliographic Details
Main Authors: Lee, T.-Y (Author), Wan, Y. (Author), Wang, Z. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Amino acid compositions
Anticancer peptides
Antimicrobial peptide
antineoplastic agent
Antineoplastic Agents
Computational tools
Diseases
human
Humans
Learning systems
machine learning
Machine Learning
Machine learning methods
neoplasm
Neoplasms
Optimization
Optimized models
peptide
Peptides
Position specific scoring matrix
Prediction tools
PSSM
Sequential minimal optimization
SMO
support vector machine
Support Vector Machine
Support vector machines
SVM
Vector spaces
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