| Item type |
SIG Technical Reports(1) |
| 公開日 |
2022-09-05 |
| タイトル |
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タイトル |
Network-based pathogenicity prediction for genomic variants |
| タイトル |
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言語 |
en |
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タイトル |
Network-based pathogenicity prediction for genomic variants |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Kyoto University |
| 著者所属 |
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Kyoto University |
| 著者所属 |
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Kyoto University |
| 著者所属 |
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Kyoto University |
| 著者所属(英) |
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en |
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Kyoto University |
| 著者所属(英) |
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en |
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Kyoto University |
| 著者所属(英) |
|
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en |
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Kyoto University |
| 著者所属(英) |
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en |
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Kyoto University |
| 著者名 |
Mayumi, Kamada
Atsuko, Takagi
Ryosuke, Kojima
Yasushi, Okuno
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| 著者名(英) |
Mayumi, Kamada
Atsuko, Takagi
Ryosuke, Kojima
Yasushi, Okuno
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Genomic medicine is expected to play a key role in realizing precision medicine. On the other hand, only a few percent of the huge number of genomic variants detected by genome analysis have clear clinical significance. Many computational methods have been developed to predict the pathogenicity of variants. Although the mechanisms of diseases involve complex biomolecular interactions, conventional methods have not been able to consider the molecular relationship. In this study, we developed PathoGN, which represents the molecular network as graphs and predicts the pathogenicity of variants using Graph Convolutional Networks. In performance evaluation using benchmark sets, PathoGN outperformed existing methods. Results of evaluation with ClinVar, a database of disease-related variants, PathoGN was shown to accurately predict the current label of a variant whose significance was unknown in the past. These results suggest the usefulness of considering biological networks. |
| 論文抄録(英) |
|
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内容記述タイプ |
Other |
|
内容記述 |
Genomic medicine is expected to play a key role in realizing precision medicine. On the other hand, only a few percent of the huge number of genomic variants detected by genome analysis have clear clinical significance. Many computational methods have been developed to predict the pathogenicity of variants. Although the mechanisms of diseases involve complex biomolecular interactions, conventional methods have not been able to consider the molecular relationship. In this study, we developed PathoGN, which represents the molecular network as graphs and predicts the pathogenicity of variants using Graph Convolutional Networks. In performance evaluation using benchmark sets, PathoGN outperformed existing methods. Results of evaluation with ClinVar, a database of disease-related variants, PathoGN was shown to accurately predict the current label of a variant whose significance was unknown in the past. These results suggest the usefulness of considering biological networks. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA12055912 |
| 書誌情報 |
研究報告バイオ情報学（BIO）
巻 2022-BIO-71,
号 3,
p. 1-2,
発行日 2022-09-05
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8590 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-BIO22071003.pdf (370.2 kB)
