[HTML][HTML] Patient similarity for precision medicine: A systematic review
Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical
practice guidelines typically define a set of recommendations together with eligibility criteria …
practice guidelines typically define a set of recommendations together with eligibility criteria …
Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system
The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …
since their reference vectors preset in advance are not always adaptable to various problem …
MOLI: multi-omics late integration with deep neural networks for drug response prediction
Motivation Historically, gene expression has been shown to be the most informative data for
drug response prediction. Recent evidence suggests that integrating additional omics can …
drug response prediction. Recent evidence suggests that integrating additional omics can …
[HTML][HTML] An unsupervised machine learning method for discovering patient clusters based on genetic signatures
Introduction Many chronic disorders have genomic etiology, disease progression, clinical
presentation, and response to treatment that vary on a patient-to-patient basis. Such …
presentation, and response to treatment that vary on a patient-to-patient basis. Such …
Evolutionary multiobjective clustering and its applications to patient stratification
Patient stratification has a major role in enabling efficient and personalized medicine. An
important task in patient stratification is to discover disease subtypes for effective treatment …
important task in patient stratification is to discover disease subtypes for effective treatment …
Evolutionary multiobjective clustering algorithms with ensemble for patient stratification
Patient stratification has been studied widely to tackle subtype diagnosis problems for
effective treatment. Due to the dimensionality curse and poor interpretability of data, there is …
effective treatment. Due to the dimensionality curse and poor interpretability of data, there is …
MoSBi: Automated signature mining for molecular stratification and subtyping
TD Rose, T Bechtler, OA Ciora… - Proceedings of the …, 2022 - National Acad Sciences
The improving access to increasing amounts of biomedical data provides completely new
chances for advanced patient stratification and disease subtyping strategies. This requires …
chances for advanced patient stratification and disease subtyping strategies. This requires …
Nature-inspired multiobjective patient stratification from cancer gene expression data
Stratifying personalized treatment for patients has been one of the main challenges for
modern medicine. To solve this problem, various clustering algorithms have been proposed …
modern medicine. To solve this problem, various clustering algorithms have been proposed …
Identification of differentially expressed gene modules in heterogeneous diseases
Motivation Identification of differentially expressed genes is necessary for unraveling
disease pathogenesis. This task is complicated by the fact that many diseases are …
disease pathogenesis. This task is complicated by the fact that many diseases are …
Substra: supervised bayesian patient stratification
S Khakabimamaghani, YD Kelkar, BM Grande… - …, 2019 - academic.oup.com
Motivation Patient stratification methods are key to the vision of precision medicine. Here, we
consider transcriptional data to segment the patient population into subsets relevant to a …
consider transcriptional data to segment the patient population into subsets relevant to a …