Identify Diabetes-related Targets based on ForgeNet_GPC.

Current computer-aided drug design Pub Date : 2024-01-01 DOI:10.2174/0115734099258183230929173855

Bin Yang, Linlin Wang, Wenzheng Bao

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Abstract

Background: Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.

Aims: Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.

Objectives: In this study, a disease target identification algorithm based on protein recognition is proposed.

Materials and methods: In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors.

Results: A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.

Conclusion: In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

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根据 ForgeNet_GPC 确定糖尿病相关目标。

背景：研究潜在的治疗靶点和新的作用机制可以大大提高新药开发的效率。目的：糖尿病等多基因遗传病是由多个基因位点和环境因素相互作用引起的：本研究提出了一种基于蛋白质识别的疾病目标识别算法：方法：从治疗糖尿病的文献数据库中收集相关和不相关的靶点。方法：该方法从治疗糖尿病的文献数据库中收集相关和不相关的靶点，查询每个靶点对应的转录蛋白，从而构建蛋白质数据集。利用六种蛋白质特征提取算法（AAC、CKSAAGP、DDE、DPC、GAAP 和 TPC）获得每个蛋白质的特征向量，并将其合并为完整的特征向量：结果：提出了一种基于 forgeNet 和高斯过程分类器（GPC）的新型分类器（forgeNet_GPC）来对蛋白质进行分类：结论：在 forgeNet_GPC 中，forgeNet 被用来选择重要的特征，而 GPC 被用来解决分类问题。实验结果表明，forgeNet_GPC 在 ROC-AUC、PR-AUC、MCC、Youden Index 和 Kappa 方面的表现优于 22 种分类器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Current computer-aided drug design

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