A network-based method for brain disease gene prediction by integrating brain connectome and molecular network

Abstract

Brain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein–protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer’s disease, Parkinson’s disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

“…This can be associated with significant synchronization and temporal coherence dysfunction, decreasing large cortical networks between the two hemispheres from binding temporally and spatially. This can result in a functional disconnection syndrome ( 88 , 102 , 135 , 137 , 279 , 289 , 290 ).…”

“…Individuals with ASD and other neurobehavioral disorders have also evidenced a reduction of interregional brain connectivity ( 25 , 27 , 43 , 44 , 94 , 95 , 102 , 134 – 139 ). The corpus callosum appears to be the brain area associated with the reduced cortical connectivity found in individuals with ASD ( 140 ).…”

Label-free Quantitative Proteomic Analysis of Cerebrospinal Fluid and Serum in Patients With Relapse-Remitting Multiple Sclerosis

“…Furthermore, the differential expression of these 16 proteins was closely related to the occurrence of RRMS. However, it is still unknown whether the differential expression of proteins is caused by these gene mutations ( Wang W. et al, 2022 ) ( Peng et al, 2021b ) ( Wang et al, 2021 ) ( Wang T. et al, 2022 ). In the future, we will link gene mutations with differential expression of proteins to study the pathogenesis of diseases.…”

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

“…The input of the binary classification model is an HLA-peptide pair, and the output of that is 1 or 0, where 1 means the peptide will bind to the HLA allele, and 0 means the peptide will not bind. Seven popular binary classifiers are used to establish the classification models, including logistic regression (LR) [ 39 ], support vector machine (SVM) [ 40 ], bagging classifier (Bagging) [ 41 ], extreme gradient boost (XGBoost) [ 42 ], k -nearest neighbor (KNN) [ 43 ], decision tree (Dtree) [ 44 ] and naive bayes (NB) [ 45 ].…”