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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xin, Xiong | - |
dc.contributor.author | Minrui, Li | - |
dc.contributor.author | Yuyan, Ren | - |
dc.date.accessioned | 2023-04-25T07:37:46Z | - |
dc.date.available | 2023-04-25T07:37:46Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s11042-022-13916-7 | - |
dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/8292 | - |
dc.description | CC BY | vi |
dc.description.abstract | Microbiome is closely related to many major human diseases, but it is generally analyzed by the traditional statistical methods such as principal component analysis, principal coordinate analysis, etc. These methods have shortcomings and do not consider the characteristics of the microbiome data itself (i.e., the “probability distribution” of microbiome). A new method based on probabilistic topic model was proposed to mine the information of gut microbiome in this paper, taking gut microbiome of type 2 diabetes patients and healthy subjects as an example. Firstly, different weights were assigned to different microbiome according to the degree of correlation between different microbiome and subjects. | vi |
dc.language.iso | en | vi |
dc.publisher | Springer | vi |
dc.subject | probabilistic topic models | vi |
dc.title | A new method for mining information of gut microbiome with probabilistic topic models | vi |
dc.type | Book | vi |
Appears in Collections | ||
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