JP2019023871A - 診断テストを特定するための経路分析 - Google Patents
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Abstract
【解決手段】1つまたは複数の病気を治療する潜在的能力を有する治療が開発されたとき、係る薬剤は病気に関する異なる細胞株に対して異なる効果を有する。機械学習システムは、治療に対する異なる細胞株の感度データとの望ましい相関関係を有する測定可能な細胞の特徴を多数の異なる測定可能な細胞の特徴から推測するようプログラムされる。機械学習システムは、次に、治療を投与することを推奨するために患者が示すべき細胞の特徴の閾値レベルを相関関係に基づいて判定するようプログラムされる。
【選択図】図1
Description
Claims (23)
- 患者の経路要素の閾値を判定するコンピュータ実施方法であって、
機械学習システムにより、複数の疾患細胞株についての複数のデータセットを受け取ることであって、それぞれのデータセットは(a)複数の既知の経路要素のオミクス・データ及び(b)薬剤に反応する疾患細胞株の感度レベルを示す感度データを含み、前記経路要素は経路モデルのメンバーである、複数の疾患細胞株についての複数のデータセットを受け取ることと、
前記機械学習システムにより、前記複数のデータセットを使用して細胞の特徴を推測することと、
前記機械学習システムにより、前記既知の経路要素のうちの1つの細胞の特徴と、前記感度データと、の間の相関関係である、複数の相関関係を判定することと、
機械学習アルゴリズムを使用して、複数の特異的な疾患細胞株を第1セットと第2セットとに最適に分割する前記既知の経路要素の1つの閾値を特定することと、
を含む方法。 - 前記感度データは、GI50値またはIC50値を含む、請求項1に記載の方法。
- 前記第1セット及び前記第2セットは、特異的な疾患細胞株間で特異的なメンバーを有する、請求項1に記載の方法。
- それぞれの相関関係についての前記複数の相関関係を判定することは、それぞれの特異的な疾患細胞株の感度データに関連付けられた前記既知の経路要素の1つの細胞の特徴を示すグラフ内のデータ点を生成することを含む、請求項1に記載の方法。
- 前記第1セット及び前記第2セットがどの程度良好に分割にされているかに基づいて、前記相関関係に信頼性スコアを割り当てること、をさらに含む、請求項4に記載の方法。
- 前記信頼性スコアは、前記閾値を超えるか前記閾値未満に位置する前記特異的な疾患細胞株の前記感度データによって判定される、請求項5に記載の方法。
- 前記細胞の特徴は、前記既知の経路要素の発現等級である、請求項1に記載の方法。
- 前記発現等級は少なくとも、複合体の濃度により定められる、請求項7に記載の方法。
- 前記発現等級は少なくとも、複数の複合体の組み合わせの濃度により定められる、請求項7に記載の方法。
- 前記発現等級は少なくとも、2つ以上の複合体間の濃度の比により定められる、請求項7に記載の方法。
- 前記患者の推奨療法を含む出力データを生成することをさらに含む、請求項1に記載の方法。
- 前記薬剤を用いて前記複数の特異的な疾患細胞株のサンプル疾患細胞をテストすることにより既知の量的な感度データを生成することをさらに含む、請求項1に記載の方法。
- 前記複数の特異的な疾患細胞株の第1セットは、前記薬剤による治療に対して感度を有する細胞株とみなされ、前記複数の特異的な疾患細胞株の第2セットは、前記薬剤による治療に対して抵抗性を有する細胞株とみなされる、請求項1に記載の方法。
- 前記オミクス・データは遺伝子コピー数データ、遺伝子突然変異データ、遺伝子メチル化データ、遺伝子発現データ、RNAスプライス情報データ、siRNAデータ、RNA翻訳データ、およびタンパク質活性データからなる群から選択された、請求項1に記載の方法。
- 患者の経路要素の閾値を判定するためのシステムであって、
それぞれ複数の特異的な疾患細胞株の複数の既知の経路要素のオミクス・データから導き出された複数の特異的なデータセットを格納する経路モデル・データベースであって、前記経路要素は、経路モデルのメンバーである、経路モデル・データベースと、
前記経路モデル・データベースに情報的に連結され、
機械学習システムにより、前記経路モデル・データベースから、複数の特異的なデータセットと、前記薬剤に反応する疾患細胞株の感度レベルを示す感度データと、を受け取ることと、
機械学習システムにより、前記複数のデータセットを使用して既知の経路要素の細胞の特徴を推測することと、
機械学習システムにより、前記既知の経路要素のうちの1つの細胞の特徴と、前記感度データと、の間の相関関係である、複数の相関関係を判定することと、
機械学習アルゴリズムを使用して、複数の特異的な疾患細胞株を第1セットと第2セットとに最適に分割する前記既知の経路要素の1つの閾値を特定することと、
を行うようにプログラムされた、コンピュータと、
を含むシステム。 - 前記感度データは、GI50値またはIC50値を含む、請求項15に記載のシステム。
- それぞれの相関関係についての前記複数の相関関係は、前記第1セット及び前記第2セットの前記特異的な疾患細胞株の感度データに関連付けられた前記既知の経路要素の1つの細胞の特徴を示すグラフ内のデータ点を生成することによって判定される、請求項16に記載のシステム。
- 前記第1セット及び前記第2セットがどの程度良好に分割にされているかに基づいて、前記相関関係に信頼性スコアを割り当てること、をさらに含む、請求項17に記載のシステム。
- 機械学習システムを含むコンピュータ・システムに1つの方法を実行させるためのプログラム命令を含む非一時的コンピュータ可読媒体であって、前記方法は、
機械学習システムにより、複数の疾患細胞株についての複数のデータセットを受け取ることであって、それぞれのデータセットは(a)複数の既知の経路要素のオミクス・データ及び(b)薬剤に反応する疾患細胞株の感度レベルを示す感度データを含み、前記経路要素は経路モデルのメンバーである、複数の疾患細胞株についての複数のデータセットを受け取ることと、
前記機械学習システムにより、前記複数のデータセットを使用して細胞の特徴を推測することと、
前記機械学習システムにより、前記既知の経路要素のうちの1つの細胞の特徴と、前記感度データと、の間の相関関係である、複数の相関関係を判定することと、
機械学習アルゴリズムを使用して、複数の特異的な疾患細胞株を第1セットと第2セットとに最適に分割する前記既知の経路要素の1つの閾値を特定することと、
を含む非一時的コンピュータ可読媒体。 - 前記感度データは、GI50値またはIC50値を含む、請求項19に記載の非一時的コンピュータ可読媒体。
- 前記第1セット及び前記第2セットは、特異的な疾患細胞株間で特異的なメンバーを有する、請求項19に記載の非一時的コンピュータ可読媒体。
- それぞれの相関関係についての前記複数の相関関係を判定することは、それぞれの特異的な疾患細胞株の感度データに関連付けられた前記既知の経路要素の1つの細胞の特徴を示すグラフ内のデータ点を生成することを含む、請求項19に記載の非一時的コンピュータ可読媒体。
- 前記第1セット及び前記第2セットがどの程度良好に分割にされているかに基づいて、前記相関関係に信頼性スコアを割り当てること、をさらに含む、請求項22に記載の非一時的コンピュータ可読媒体。
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TWI787500B (zh) | 2018-04-23 | 2022-12-21 | 美商南特細胞公司 | 新抗原表位疫苗及免疫刺激組合物及方法 |
US11915832B2 (en) | 2018-12-24 | 2024-02-27 | Medirita | Apparatus and method for processing multi-omics data for discovering new drug candidate substance |
US11721441B2 (en) * | 2019-01-15 | 2023-08-08 | Merative Us L.P. | Determining drug effectiveness ranking for a patient using machine learning |
KR20210096402A (ko) * | 2020-01-28 | 2021-08-05 | (주)인테그로메디랩 | 증례 데이터 기반 천연물 추천 장치 및 방법 |
JP6810294B1 (ja) * | 2020-06-30 | 2021-01-06 | 凸版印刷株式会社 | 評価システム、学習装置、予測装置、評価方法、及びプログラム |
WO2022196971A1 (ko) * | 2021-03-18 | 2022-09-22 | 주식회사 온코크로스 | 세포 레벨의 정보로부터 조직 레벨의 정보를 추정하는 방법 및 그 장치 |
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EP3014505A4 (en) | 2017-03-08 |
AU2016273897A1 (en) | 2017-01-12 |
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CA2919768C (en) | 2019-12-03 |
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JP2017097884A (ja) | 2017-06-01 |
US11011273B2 (en) | 2021-05-18 |
KR20160084363A (ko) | 2016-07-13 |
CN105706097A (zh) | 2016-06-22 |
JP6677773B2 (ja) | 2020-04-08 |
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