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SBSM-Pro: support bio-sequence machine for proteins

delete2024-10-22
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PRE
AI
王
王以政 (Yizheng Wang)
Y
Yixiao Zhai
丁漪杰 封面图
丁漪杰 (Yijie Ding)
Q
Quan Zou *
DOI:10.1007/s11432-024-4171-9delete
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摘要

摘要

En 中文
Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We introduce the support bio-sequence machine for proteins (SBSM-Pro), a model purpose-built for the classification of biological sequences. This model starts with raw sequences and groups amino acids based on their physicochemical properties. It incorporates sequence alignment to measure the similarities between proteins and uses a novel multiple kernel learning (MKL) approach to integrate various types of information, utilizing support vector machines for classification prediction. The results indicate that our model demonstrates commendable performance across ten datasets in terms of the identification of protein function and post translational modification. This research not only exemplifies state-of-the-art work in protein classification but also paves avenues for new directions in this domain, representing a beneficial endeavor in the development of platforms tailored for the classification of biological sequences. SBSM-Pro is available for access at http://lab.malab.cn/soft/SBSM-Pro/.
Keyword:
protein classification
machine learning
multiple kernel learning
sequence alignment

期刊

Science China Information Sciences 封面图
Science China Information Sciences
IF:
7.6
论文数:
4.9K
被引数:
8.9K

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