DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks

Abstract

Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and timeconsuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.

Using machine learning to realize genetic site screening and genomic prediction of productive traits in pigs

“…Compared with other ML algorithms, SVM is very powerful for identifying subtle patterns in small samples or complex datasets. SVMs have been successfully used in different types of biological datasets, including microarray expression data, 7,8 DNA and protein sequences, 9 protein–protein interaction networks, 10,11 tandem mass spectrometry, 12 etc. In the field of animal and plant breeding, SVMs have also been utilized for genome prediction 13 .…”

Gm-PLoc: A Subcellular Localization Model of Multi-Label Protein Based on GAN and DeepFM

“…Previous researches show that almost all life phenomena are closely related to the structure and function of proteins, and the correct subcellular localization of proteins can assist biologists in understanding proteins ( Wei et al, 2018 ; Zhang et al, 2021 ). Simultaneously, the subcellular localization of proteins also plays an essential role in disease diagnosis, drug design, and other biological researches ( Li et al, 2020 ; Wang et al, 2021 ). Biological experiments ( Murphy et al, 2000 ) were widely used to annotate protein subcellular localizations in the early stage of research.…”

Bird Eye View of Protein Subcellular Localization Prediction

“…To date, many efforts have been made in this regard. Based on different kinds of characteristics, several machine learning approaches have been developed such as neural networks [ 5 , 6 ], hidden Markov models [ 7 , 8 , 9 ], support vector machines [ 10 , 11 , 12 ], deep learning [ 13 , 14 , 15 ], random forest [ 16 ], and extreme gradient boosting [ 17 ] for prediction of subcellular localization of proteins.…”