A novel prediction method for lncRNA-disease association

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Mirhashemi AmirHossein; Vosoughifar Mahsa

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON MODERN TECHNOLOGY IN SCIENCES
Year:2023 | Issue:3

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Title

A novel prediction method for lncRNA-disease association

Author(s)

Mirhashemi AmirHossein | Vosoughifar Mahsa

Keywords

LncRNA

disease

Link prediction

preferential attachment algorithm

bipartite network

data mining

Abstract

Many long-non-coding RNAs (LncRNAs) have found in recent years, and growing evidence suggests that LncRNAs play crucial roles in a variety of biological processes, including gene expression control and Xchromosome inactivation[1], [2]. As a result, LncRNA malfunction and mutations are linked to a wide range of disorders, including breast cancer, leukemia, and many others[3], [2]. The development of powerful computational models for the identification of purported disease-related LncRNAs would help biomarker identification and drug discovery for human disease diagnosis, therapy, prognosis, and prevention[3]. As a computational graph analysis tool, Link prediction can help us get new perspectives into the network. In this paper, we studied computational Link prediction approaches to predict novel LncRNA-disease links after constructing a network between LncRNAs and diseases using a https: //www. cuilab. cn/LncRNAdisease. In accordance with computational and experimental evidence, the preferential attachment (PA) algorithm is the most reliable method for relation foresight among the popular scoring methods based on a network topology that recent publications have supported its computational predictions. According to the PA prediction, some of the LncRNA-disease connections that have already been noted in some published articles are H19-Myocardial infaraction[4], CDKN2B-AS1-Tumor[5] and Malat1-Giloma[6]. These findings suggest that PA prediction could be a promising method for generating embeddings for possibly novel LncRNA-disease relationships. Meanwhile, PVT1-Hereditary hemorrhagic telangiectasia, UBE3A-ATS-Down syndrome, NEAT-1-Hereditary hemorrhagic telangiectasia and DISC2-melanoma, are some of the likely candidates for additional laboratory and validation studies that have not been mentioned in any published article. Furthermore, it is possible to upgrade the forecasting algorithms in the future to generate new predictions.

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APA: Copy

Mirhashemi, AmirHossein, & Vosoughifar, Mahsa. (2023). A novel prediction method for lncRNA-disease association. INTERNATIONAL CONFERENCE ON MODERN TECHNOLOGY IN SCIENCES. SID. https://sid.ir/paper/1050430/en

Vancouver: Copy

Mirhashemi AmirHossein, Vosoughifar Mahsa. A novel prediction method for lncRNA-disease association. 2023. Available from: https://sid.ir/paper/1050430/en

IEEE: Copy

AmirHossein Mirhashemi, and Mahsa Vosoughifar, “A novel prediction method for lncRNA-disease association,” presented at the INTERNATIONAL CONFERENCE ON MODERN TECHNOLOGY IN SCIENCES. 2023, [Online]. Available: https://sid.ir/paper/1050430/en

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