High-Dimensional Precision Medicine From Patient-Derived Xenografts | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

Pencarian berdasarkan :

Pencarian terakhir:

Text

High-Dimensional Precision Medicine From Patient-Derived Xenografts

Naim U. Rashid - Nama Orang; Zhang, Yunshu - Nama Orang; Wang, Longshaokan - Nama Orang; Lawson, Michael T. - Nama Orang; Chen, Jingxiang - Nama Orang; Yeh, Jen Jen - Nama Orang; Luckett, Daniel J. - Nama Orang; Laber, Eric B. - Nama Orang; Zeng, Donglin - Nama Orang; Kosorok, Michael R. - Nama Orang; Liu, Yufeng - Nama Orang;

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

Ketersediaan

Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art140030	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.116 No.535 September 2021
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 1140 - 1154
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: MACHINE LEARNING
Q-LEARNING
PRECISION MEDICINE
BIOMARKERS
DEEP LEARNING AUTOENCODERS
OUTCOME WEIGHTED LEARNING
Info Detail Spesifik: https://doi.org/10.1080/01621459.2020.1828091
Pernyataan Tanggungjawab: Naim U. Rashid, Daniel J. Luckett, Jingxiang Chen, Michael T. Lawson, Longshaokan Wang, Yunshu Zhang, Eric B. Laber, Yufeng Liu, Jen Jen Yeh, Donglin Zeng, Michael R. Kosorok

Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas

Tidak Ada Data

Komentar

Anda harus masuk sebelum memberikan komentar