Disease prediction by network information gain on a single sample basis
There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Fundamental Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667325823000316 |
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| author | Jinling Yan Peiluan Li Ying Li Rong Gao Cheng Bi Luonan Chen |
| author_facet | Jinling Yan Peiluan Li Ying Li Rong Gao Cheng Bi Luonan Chen |
| author_sort | Jinling Yan |
| collection | DOAJ |
| description | There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration solely based on a single sample is a difficult problem. In this study, we presented the network information gain (NIG) method, for predicting the critical transitions or disease state based on network flow entropy from omics data of each individual. NIG can not only efficiently predict disease deteriorations but also detect their dynamic network biomarkers on an individual basis and further identify potential therapeutic targets. The numerical simulation demonstrates the effectiveness of NIG. Moreover, our method was validated by successfully predicting disease deteriorations and identifying their potential therapeutic targets from four real omics datasets, i.e., an influenza dataset and three cancer datasets. |
| format | Article |
| id | doaj-art-c6ff2558088f4085b771a5c0181eec8d |
| institution | Kabale University |
| issn | 2667-3258 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Fundamental Research |
| spelling | doaj-art-c6ff2558088f4085b771a5c0181eec8d2025-01-29T05:02:30ZengKeAi Communications Co. Ltd.Fundamental Research2667-32582025-01-0151215227Disease prediction by network information gain on a single sample basisJinling Yan0Peiluan Li1Ying Li2Rong Gao3Cheng Bi4Luonan Chen5School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China; Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China; Longmen Laboratory, Luoyang 471003, China; Corresponding authors.School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaKey Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, ChinaKey Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China; Guangdong Institute of Intelligence Science and Technology, Zhuhai 519031, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Corresponding authors.There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration solely based on a single sample is a difficult problem. In this study, we presented the network information gain (NIG) method, for predicting the critical transitions or disease state based on network flow entropy from omics data of each individual. NIG can not only efficiently predict disease deteriorations but also detect their dynamic network biomarkers on an individual basis and further identify potential therapeutic targets. The numerical simulation demonstrates the effectiveness of NIG. Moreover, our method was validated by successfully predicting disease deteriorations and identifying their potential therapeutic targets from four real omics datasets, i.e., an influenza dataset and three cancer datasets.http://www.sciencedirect.com/science/article/pii/S2667325823000316Tipping pointNetwork information gainDynamic network biomarkerDisease predictionNetwork flow entropyDrug target |
| spellingShingle | Jinling Yan Peiluan Li Ying Li Rong Gao Cheng Bi Luonan Chen Disease prediction by network information gain on a single sample basis Fundamental Research Tipping point Network information gain Dynamic network biomarker Disease prediction Network flow entropy Drug target |
| title | Disease prediction by network information gain on a single sample basis |
| title_full | Disease prediction by network information gain on a single sample basis |
| title_fullStr | Disease prediction by network information gain on a single sample basis |
| title_full_unstemmed | Disease prediction by network information gain on a single sample basis |
| title_short | Disease prediction by network information gain on a single sample basis |
| title_sort | disease prediction by network information gain on a single sample basis |
| topic | Tipping point Network information gain Dynamic network biomarker Disease prediction Network flow entropy Drug target |
| url | http://www.sciencedirect.com/science/article/pii/S2667325823000316 |
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