Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines
Background Recent advances in genome technologies and the subsequent collection of
genomic information at various molecular resolutions hold promise to accelerate the …
genomic information at various molecular resolutions hold promise to accelerate the …
PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine
N Eyal-Altman, M Last, E Rubin - BMC bioinformatics, 2017 - Springer
Background Numerous publications attempt to predict cancer survival outcome from gene
expression data using machine-learning methods. A direct comparison of these works is …
expression data using machine-learning methods. A direct comparison of these works is …
Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression
Background Important objectives in cancer research are the prediction of a patient's risk
based on molecular measurements such as gene expression data and the identification of …
based on molecular measurements such as gene expression data and the identification of …
Assessment of performance of survival prediction models for cancer prognosis
HC Chen, RL Kodell, KF Cheng, JJ Chen - BMC medical research …, 2012 - Springer
Background Cancer survival studies are commonly analyzed using survival-time prediction
models for cancer prognosis. A number of different performance metrics are used to …
models for cancer prognosis. A number of different performance metrics are used to …
The spike-and-slab lasso Cox model for survival prediction and associated genes detection
Z Tang, Y Shen, X Zhang, N Yi - Bioinformatics, 2017 - academic.oup.com
Motivation Large-scale molecular profiling data have offered extraordinary opportunities to
improve survival prediction of cancers and other diseases and to detect disease associated …
improve survival prediction of cancers and other diseases and to detect disease associated …
Using proteomics for stratification and risk prediction in patients with solid tumors
T Werner, M Fahrner, O Schilling - Die Pathologie, 2023 - Springer
Proteomics, the study of proteins and their functions, has greatly evolved due to advances in
analytical chemistry and computational biology. Unlike genomics or transcriptomics …
analytical chemistry and computational biology. Unlike genomics or transcriptomics …
Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data
L Neums, R Meier, DC Koestler… - PACIFIC SYMPOSIUM …, 2019 - World Scientific
The accurate prediction of a cancer patient's risk of progression or death can guide
clinicians in the selection of treatment and help patients in planning personal affairs …
clinicians in the selection of treatment and help patients in planning personal affairs …
Personalized integrated network modeling of the cancer proteome atlas
Personalized (patient-specific) approaches have recently emerged with a precision
medicine paradigm that acknowledges the fact that molecular pathway structures and …
medicine paradigm that acknowledges the fact that molecular pathway structures and …
Computational prediction of cancer-gene function
Most cancer genes remain functionally uncharacterized in the physiological context of
disease development. High-throughput molecular profiling and interaction studies are …
disease development. High-throughput molecular profiling and interaction studies are …
Tutorial on survival modeling with applications to omics data
Motivation Identification of genomic, molecular and clinical markers prognostic of patient
survival is important for developing personalized disease prevention, diagnostic and …
survival is important for developing personalized disease prevention, diagnostic and …