Summary of Lncrna-disease Association Prediction Method Based on Heterogeneous Information Completion and Convolutional Neural Network, by Wen-yu Xi et al.
LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network
by Wen-Yu Xi, Juan Wang, Yu-Lin Zhang, Jin-Xing Liu, Yin-Lian Gao
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a deep learning model called HCNNLDA for predicting lncRNA-disease associations (LDAs). It’s a crucial task for warning and treating complex human diseases. The existing methods have limitations in identifying nonlinear LDAs, making it challenging to predict new ones. To address this, the authors develop a model that combines a heterogeneous network with convolutional neural networks (CNNs) and XGBoost classifier. They construct a network containing lncRNA, disease, and miRNA nodes and learn low-dimensional feature representations using CNNs. The model is trained on a dataset and achieves high AUC and AUPR values under 5-fold cross-validation, outperforming several latest prediction models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to predict which long non-coding RNAs (lncRNAs) are linked to certain diseases. This is important because lncRNAs can help us understand how diseases work and find new ways to treat them. The researchers use special computer algorithms to analyze large amounts of data and identify patterns that might not be visible by looking at the data alone. They create a model that combines different types of information about lncRNAs, diseases, and small RNAs called miRNAs. This model can predict which lncRNAs are linked to certain diseases with high accuracy. The results show that this new method is better than previous methods for predicting lncRNA-disease associations. |
Keywords
» Artificial intelligence » Auc » Deep learning » Xgboost
