Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches
B Güvenç Paltun, H Mamitsuka… - Briefings in …, 2021 - academic.oup.com
Predicting the response of cancer cell lines to specific drugs is one of the central problems in
personalized medicine, where the cell lines show diverse characteristics. Researchers have …
personalized medicine, where the cell lines show diverse characteristics. Researchers have …
scVAE: variational auto-encoders for single-cell gene expression data
CH Grønbech, MF Vording, PN Timshel… - …, 2020 - academic.oup.com
Motivation Models for analysing and making relevant biological inferences from massive
amounts of complex single-cell transcriptomic data typically require several individual data …
amounts of complex single-cell transcriptomic data typically require several individual data …
Attribute-aware recommender system based on collaborative filtering: Survey and classification
Attribute-aware CF models aim at rating prediction given not only the historical rating given
by users to items but also the information associated with users (eg, age), items (eg, price) …
by users to items but also the information associated with users (eg, age), items (eg, price) …
DRIM: a web-based system for investigating drug response at the molecular level by condition-specific multi-omics data integration
Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus,
understanding the effect of drug at the molecular level can be helpful in both drug discovery …
understanding the effect of drug at the molecular level can be helpful in both drug discovery …
Comparative study of inference methods for bayesian nonnegative matrix factorisation
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix
factorisation methods, which are commonly used for predicting missing values, and for …
factorisation methods, which are commonly used for predicting missing values, and for …
Diverse: Bayesian data integrative learning for precise drug response prediction
Detecting predictive biomarkers from multi-omics data is important for precision medicine, to
improve diagnostics of complex diseases and for better treatments. This needs substantial …
improve diagnostics of complex diseases and for better treatments. This needs substantial …
SEMCM: a self-expressive matrix completion model for anti-cancer drug sensitivity prediction
L Zhang, Y Yuan, J Yu, H Liu - Current Bioinformatics, 2022 - ingentaconnect.com
Background: Genomic data sets generated by several recent large scale high-throughput
screening efforts pose a complex computational challenge for anticancer drug sensitivity …
screening efforts pose a complex computational challenge for anticancer drug sensitivity …
Attribute-aware collaborative filtering: survey and classification
Attribute-aware CF models aims at rating prediction given not only the historical rating from
users to items, but also the information associated with users (eg age), items (eg price), or …
users to items, but also the information associated with users (eg age), items (eg price), or …
Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes
Accurate identification of pathways associated with cancer phenotypes (eg, cancer subtypes
and treatment outcomes) could lead to discovering reliable prognostic and/or predictive …
and treatment outcomes) could lead to discovering reliable prognostic and/or predictive …
Computational Approaches for Exploring the Relationships in High Dimensional Spaces of Multi-Omics Data Utilizing Biological Prior Knowledge
오민식 - 2021 - s-space.snu.ac.kr
Understanding how cells function or respond to external stimuli is one of the most important
questions in biology and medicine. Thanks to the advances in instrumental technologies …
questions in biology and medicine. Thanks to the advances in instrumental technologies …