DeepECMP: Predicting Extracellular Matrix Proteins using Deep Learning
This addresses a bioinformatics problem for researchers by providing a more accurate and efficient tool for ECM protein prediction, though it is incremental as it applies deep learning to an existing task.
The paper tackled predicting extracellular matrix proteins by developing DeepECMP, a deep learning model using an ensemble of neural networks, achieving 83.6% balanced accuracy and outperforming existing algorithms.
Introduction: The extracellular matrix (ECM) is a networkof proteins and carbohydrates that has a structural and bio-chemical function. The ECM plays an important role in dif-ferentiation, migration and signaling. Several studies havepredicted ECM proteins using machine learning algorithmssuch as Random Forests, K-nearest neighbours and supportvector machines but is yet to be explored using deep learn-ing. Method: DeepECMP was developed using several previ-ously used ECM datasets, asymmetric undersampling andan ensemble of 11 feed-forward neural networks. Results: The performance of DeepECMP was 83.6% bal-anced accuracy which outperformed several algorithms. Inaddition, the pipeline of DeepECMP has been shown to behighly efficient. Conclusion: This paper is the first to focus on utilizingdeep learning for ECM prediction. Several limitations areovercome by DeepECMP such as computational expense,availability to the public and usability outside of the humanspecies