返回
SBSM-Pro: support bio-sequence machine for proteins
DOI:10.1007/s11432-024-4171-9.png)
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
IF:
7.6
论文数:
4.9K
被引数:
8.9K
机构
暂无机构信息
引用论文
Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sitesDeepro-glu: 使用ProtBert和手工制作的特征结合卷积神经网络和bi-lstm模型来识别赖氨酸谷氨酰胺化位点
Distinct 5-methylcytosine profiles in poly(A) RNA from mouse embryonic stem cells and brain
GENOME BIOLOGY
IF9.4
Multiple kernel learning with hybrid kernel alignment maximization基于混合核对齐最大化的多核学习
PATTERN RECOGNITION
IF7.6

