# SBSM-Pro: Support Bio-sequence Machine for Proteins

arXiv: https://arxiv.org/abs/2308.10275 · HTML: https://mlsanity.com/paper/2308.10275/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 69 references that are in the map.
- First posted on arXiv 20 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review](https://mlsanity.com/paper/2309.06388/) (2023)
- [Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction](https://mlsanity.com/paper/2407.00105/) (2024)

## Abstract (arXiv)

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 posttranslational 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/.