@phdthesis{oai:doshisha.repo.nii.ac.jp:00028866,
 author = {福島, 亜梨花 and Fukushima, Arika},
 month = {2022-04-18, 2023-03-02},
 note = {バイオマーカーはある特定の疾病や体質に関与する生体内分子のことであり，個別化医療の実現に貢献する．時系列の遺伝子発現プロファイルはバイオマーカーの探索に有用であるが，サンプルサイズに対して遺伝子数が多い，遺伝子間の多重共線性，時系列の複雑さなどの課題があった．本研究では，これらの課題に対応した時系列の遺伝子プロファイルを用いたバイオマーカーの探索手法を提案し，その有効性を示した．, Biomarkers contribute to designing the therapy. Time-course gene expression profiles were useful for biomarker discovery. However, biomarker discovery using time-course gene expression profiles suffered from three difficulties, including (1) high dimensional data with small sample size and a large number of genes, (2) multi-collinearity among genes, and (3) complexity of time-course. To solve these problems, expanding elastic net for consistent differentiation (eENCD) and consolidating probabilities of multiple time points (CPMTP) as new biomarker discovery methods using time-course gene expression profiles, were proposed and evaluated., application/pdf},
 title = {Prediction method for therapeutic response at multiple time points of gene expression profiles},
 year = {},
 yomi = {フクシマ, アリカ}
}