HumanomeLab / Mlcourse
生命情報の機械学習入門（新学術領域「先進ゲノム支援」中級講習会資料）
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生命情報・バイオインフォマティックス向けの機械学習入門
文責：瀬々 潤（ヒューマノーム研究所 ＆ 産総研AIRC）
この資料は、新学術領域「先進ゲノム支援」中級講習 (2018年11月実施) のために作成しました。
このコースでは、機械学習を利用したクラス分類問題を、実問題を利用して一通り問いてみることに焦点をあてます。 まず前半は、酵母の表現型データベースである、SCMD (Saccaromices cerevisiae Morphology Database) (Ohya, Sese et al. PNAS 2005, Saito et al. NAR 2004) のデータを利用して、酵母の画像（および、画像から抽出した特徴量）から、各細胞の細胞周期を同定する機械学習を実施します。
後半は、 世界のChIP-seqデータ(転写因子結合サイト実験)を収集し解析したデータベースであるChIP-Atlas (Oki et al. EMBO reports 2018) から、一次解析の終了したChIP-seqデータを基に、転写因子結合の有無を予測します。
- 0章 : データの準備、可視化
- 1章 : 特徴量を利用した機械学習(SVM, Random Forest)
- 2章 : 特徴量を利用した深層学習（PyTorchを利用して）
- 3章 : 画像を解析する深層学習
- 4章 : 配列を解析する深層学習
更新履歴： 2018/11/21: 初版作成
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