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Drug toxicity prediction model based on enhanced graph neural network.

Comput Biol Med

December 2024

Faculty of Computer and AI, Cairo University, Egypt. Electronic address:

Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms struggle to capture the full scope of molecular structure features, limiting their effectiveness in toxicity prediction. Graph Neural Network offers a promising solution by effectively extracting drug features from their molecular graphs.

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We derive exact equations for the spectral density of sparse networks with an arbitrary distribution of the number of single edges and triangles per node. These equations enable a systematic investigation of the effects of clustering on the spectral properties of the network adjacency matrix. In the case of heterogeneous networks, we demonstrate that the spectral density becomes more symmetric as the fluctuations in the triangle-degree sequence increase.

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In this paper, we consider two fundamental cut approximation problems on large graphs. We prove new lower bounds for both problems that are optimal up to logarithmic factors. The first problem is to approximate cuts in balanced directed graphs.

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Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics.

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Shortest path counting in complex networks based on powers of the adjacency matrix.

Chaos

October 2024

Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China.

Article Synopsis
  • Complex networks, which consist of interconnected nodes and edges, are vital in understanding various systems in nature and society, with a focus on shortest path calculations in network science.
  • The study extends the use of the adjacency matrix from counting walks to counting shortest paths, addressing the challenge of calculating both the number and length of these paths effectively.
  • The proposed algorithm, tested on both synthetic and real-world networks, is significantly faster and more efficient than traditional methods, with potential applications in areas like traffic optimization and social network analysis.
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