Applications of the inverse Ising problem to biological data analysis
大規模な生命情報データの表現方法として、サンプルを行、計測した各要素を列とする行列形式は典型的な表現方法である。またこの行列データから相関関係や依存関係にある列のペアを検出する解析は、一般的なデータ解析手法であるといえる。この相関関係や依存関係を定量化する手法として、相関係数や相互情報量といった指標がよく利用されるが、これらの指標は擬似相関や偽陽性を多く検出する危険があることが知られている。近年では、このような偽陽性を防ぐための手法として生成モデルに基づくアプローチが利用されており、特にデータのとる値が離散(カテゴリカル)である場合は逆イジング法と呼ばれる。本総説では、この逆イジング法のモデル...
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| Published in | JSBi Bioinformatics Review Vol. 1; no. 1; pp. 3 - 11 |
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| Main Author | |
| Format | Journal Article |
| Language | Japanese |
| Published |
Japanese Society for Bioinformatics
2020
特定非営利活動法人 日本バイオインフォマティクス学会 |
| Online Access | Get full text |
| ISSN | 2435-7022 |
| DOI | 10.11234/jsbibr.2020.1 |
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| Abstract | 大規模な生命情報データの表現方法として、サンプルを行、計測した各要素を列とする行列形式は典型的な表現方法である。またこの行列データから相関関係や依存関係にある列のペアを検出する解析は、一般的なデータ解析手法であるといえる。この相関関係や依存関係を定量化する手法として、相関係数や相互情報量といった指標がよく利用されるが、これらの指標は擬似相関や偽陽性を多く検出する危険があることが知られている。近年では、このような偽陽性を防ぐための手法として生成モデルに基づくアプローチが利用されており、特にデータのとる値が離散(カテゴリカル)である場合は逆イジング法と呼ばれる。本総説では、この逆イジング法のモデルとパラメータの学習方法、およびタンパク質構造解析を中心とした生命情報データ解析への応用例について紹介し、今後の展開について議論する。 |
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| AbstractList | 大規模な生命情報データの表現方法として、サンプルを行、計測した各要素を列とする行列形式は典型的な表現方法である。またこの行列データから相関関係や依存関係にある列のペアを検出する解析は、一般的なデータ解析手法であるといえる。この相関関係や依存関係を定量化する手法として、相関係数や相互情報量といった指標がよく利用されるが、これらの指標は擬似相関や偽陽性を多く検出する危険があることが知られている。近年では、このような偽陽性を防ぐための手法として生成モデルに基づくアプローチが利用されており、特にデータのとる値が離散(カテゴリカル)である場合は逆イジング法と呼ばれる。本総説では、この逆イジング法のモデルとパラメータの学習方法、およびタンパク質構造解析を中心とした生命情報データ解析への応用例について紹介し、今後の展開について議論する。 |
| Author | Fukunaga, Tsukasa |
| Author_FL | 福永 津嵩 |
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| Author_xml | – sequence: 1 fullname: Fukunaga, Tsukasa organization: Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo |
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