TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types
Motivation Clinical response to anti-cancer drugs varies between patients. A large portion of
this variation can be explained by differences in molecular features, such as mutation status …
this variation can be explained by differences in molecular features, such as mutation status …
Multifactorial drug response modeling based on cancer organoid data.
e13544 Background: Prediction of drug response based on cancer molecular profiles is of
paramount importance for precision oncology. Most existing drug response prediction …
paramount importance for precision oncology. Most existing drug response prediction …
[图书][B] Latent-variable models for drug response prediction and genetic testing
L Rampasek - 2020 - search.proquest.com
High-throughput DNA sequencing and related biotechnologies revolutionized our
understanding of human genomics and diseases with genetic component, particularly of …
understanding of human genomics and diseases with genetic component, particularly of …
A survey and systematic assessment of computational methods for drug response prediction
J Chen, L Zhang - Briefings in bioinformatics, 2021 - academic.oup.com
Drug response prediction arises from both basic and clinical research of personalized
therapy, as well as drug discovery for cancers. With gene expression profiles and other …
therapy, as well as drug discovery for cancers. With gene expression profiles and other …
Addressing genetic tumor heterogeneity through computationally predictive combination therapy
Recent tumor sequencing data suggest an urgent need to develop a methodology to directly
address intratumoral heterogeneity in the design of anticancer treatment regimens. We use …
address intratumoral heterogeneity in the design of anticancer treatment regimens. We use …
Artificial intelligence for drug response prediction in disease models
Accumulated preclinical data are increasingly being re-used to build and validate predictive
models generated by artificial intelligence (AI)[1] algorithms. Such in silico models have a …
models generated by artificial intelligence (AI)[1] algorithms. Such in silico models have a …
Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach
A Emdadi, C Eslahchi - Journal of Bioinformatics and …, 2022 - World Scientific
Predicting tumor drug response using cancer cell line drug response values for a large
number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting …
number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting …
Impact of between-tissue differences on pan-cancer predictions of drug sensitivity
Increased availability of drug response and genomics data for many tumor cell lines has
accelerated the development of pan-cancer prediction models of drug response. However, it …
accelerated the development of pan-cancer prediction models of drug response. However, it …
Single agent response comparisons in a large-scale, preclinical trial of rare cancer PDXs by the National Cancer Institute's patient-derived models repository
YA Evrard, SY Alcoser, S Borgel, D Breen, J Carter… - Cancer Research, 2021 - AACR
Abstract The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR;
https://pdmr. cancer. gov) is performing a large-scale preclinical study with 39 patient …
https://pdmr. cancer. gov) is performing a large-scale preclinical study with 39 patient …
Predictive modeling of anti-cancer drug sensitivity from genetic characterizations
Accurately predicting sensitivity of tumor cells to anti-cancer drugs based on genetic
characterizations is a significant challenge for personalized cancer therapy. This chapter …
characterizations is a significant challenge for personalized cancer therapy. This chapter …