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An integrated network topology and deep learning model for prediction of Alzheimer disease candidate genes

  • Application of soft computing
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Abstract

Alzheimer’s disease (AD) is a neurological illness that causes short-term memory loss. There are currently no viable therapeutic therapies for this condition that can cure it. The source of Alzheimer’s disease is unknown. However, genetic factors are thought to have a role in the illness’s development, with about 70% of the disease’s risk attributed to the vast number of genes associated. Despite discovering several potential AD susceptibility genes through genetic association studies, there is a more significant challenge to identify unidentified AD-associated genes and drug targets to gain a good insight into the disease-causing mechanisms of Alzheimer’s disease and develop effective AD therapeutics. The proposed DC-GC (Degree Centrality- Graph Colouring) model brings an accuracy of 96% for (Artificial Neural Network) ANN model, 87.3% for KNN (K-Nearest Neighbourhood classifier) classifier, 86% for SVM (Support Vector Machine) classifier, 85.3% than Decision Tree. It is visible; the network topology model performs well for ANN classifier than other existing models. Similarly, the model also brings a sensitivity measure of 97% for the ANN model, 84% for KNN (K-Nearest Neighbourhood classifier), 84.2% for SVM (Support Vector Machine) classifier and 84% for the Decision tree classifier. In this research work, a novel network topology measure DC-GC (Degree Centrality- Graph Colouring) and intelligent-based machine learning models are used for identifying candidate genes from protein–protein interaction and sequence features of genes. The integrated method helps to identify the target gene for Alzheimer’s disease by evaluating the connectivity between the genes and the physicochemical properties of the genes. The approach helps to rank the genes according to the property that adjacency genes should not share the same colour. The DC-GC (Degree Centrality-Graph Colouring)-based network topology measure provides remarkable improvement over existing centrality measures. The integration of network topology measure with the SVM (Support Vector Machine) model gave promising results of 96% accuracy, 97% sensitivity, 98% specificity, 96% PPV (Positive Predictive Value), 95% NPV (Negative Predictive Value) and 97% F-score.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

AD:

Alzheimer’s disease

PPI:

Protein–protein interaction

DC:

GC degree centrality—graph colouring

DC:

Degree centrality

BC:

Betweenness centrality

CC:

Closeness centrality

EC:

Eigen vector centrality

NC:

Network centrality

SC:

Subgraph centrality

IC:

Information centrality

KNN:

K-nearest neighbourhood

SVM:

Support vector machine

PPV:

Positive predictive value

NPV:

Negative predictive value

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Funding

There was no financial support received from any organization for carrying out this work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore, India

    Naveen Sundar Gnanadesigan & Narmadha Dhanasegar

  2. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology (Autonomous), Coimbatore, Tamil Nadu, India

    Manjula Devi Ramasamy

  3. Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India

    Suresh Muthusamy

  4. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India

    Om Prava Mishra

  5. Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India

    Ganesh Kumar Pugalendhi

  6. Department of Information Technology, Panimalar Engineering College (Autonomous), Poonamallee, Chennai, Tamil Nadu, India

    Suma Christal Mary Sundararajan

  7. Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology (Autonomous), Sathyamangalam, Erode, Tamil Nadu, India

    Ashokkumar Ravindaran

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  1. Naveen Sundar Gnanadesigan
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Correspondence to Suresh Muthusamy.

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Gnanadesigan, N.S., Dhanasegar, N., Ramasamy, M.D. et al. An integrated network topology and deep learning model for prediction of Alzheimer disease candidate genes. Soft Comput 27, 14189–14203 (2023). https://doi.org/10.1007/s00500-023-08390-8

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