Knowledge-guided statistical learning methods for analysis of high-dimensional-omics data in precision oncology
High-dimensional-omics data such as genomic, transcriptomic, and metabolomic data offer
great promise in advancing precision medicine. In particular, such data have enabled the …
great promise in advancing precision medicine. In particular, such data have enabled the …
Dissociation Time, Quantum Yield, and Dynamic Reaction Pathways in the Thermolysis of trans-3,4-Dimethyl-1,2-dioxetane
The thermolysis of trans-3, 4-dimethyl-1, 2-dioxetane is studied by trajectory surface
hopping. The significant difference between long and short dissociation times is rationalized …
hopping. The significant difference between long and short dissociation times is rationalized …
Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease
R Lyu, M Vannucci, S Kundu - Neuroinformatics, 2024 - Springer
Tensor-based representations are being increasingly used to represent complex data types
such as imaging data, due to their appealing properties such as dimension reduction and …
such as imaging data, due to their appealing properties such as dimension reduction and …
Scalable Knowledge Graph Construction and Inference on Human Genome Variants
Real-world knowledge can be represented as a graph consisting of entities and
relationships between the entities. The need for efficient and scalable solutions arises when …
relationships between the entities. The need for efficient and scalable solutions arises when …
Bayesian non-linear support vector machine for high-dimensional data with incorporation of graph information on features
Support vector machine (SVM) is a popular classification method for analysis of high
dimensional data such as genomics data. Recently a number of linear SVM methods have …
dimensional data such as genomics data. Recently a number of linear SVM methods have …
Bayesian pathway analysis over brain network mediators for survival data
Technological advancements in noninvasive imaging facilitate the construction of whole
brain interconnected networks, known as brain connectivity. Existing approaches to analyze …
brain interconnected networks, known as brain connectivity. Existing approaches to analyze …
Joint bayesian variable selection and graph estimation for non-linear SVM with application to genomics data
Support vector machine (SVM) is a powerful classification tool for analysis of high
dimensional data such as genomics. Regularized linear and nonlinear SVM methods with …
dimensional data such as genomics. Regularized linear and nonlinear SVM methods with …
[图书][B] Sobre Computação Quântica
MR dos Santos - 2023 - books.google.com
Este será um diálogo no estilo platônico entre personagens fictícios falando sobre
computação quântica, embora esse seja mesmo o tema central da conversa, a verdade é …
computação quântica, embora esse seja mesmo o tema central da conversa, a verdade é …
Integrating five feature types extracted from ultrasonograms to improve the prediction of thyroid papillary carcinoma
R Zhu, Z Wang, Y Zhang, B Yu, M Qi, X Feng… - IEEE …, 2019 - ieeexplore.ieee.org
Ultrasonogram is one of the main techniques for the non-invasive observation and the
diagnosis of the thyroid gland. And, the thyroid papillary carcinoma (TPC) was usually …
diagnosis of the thyroid gland. And, the thyroid papillary carcinoma (TPC) was usually …
[图书][B] Sparse Bayesian inference using reduced-rank regression approaches
D Yang - 2022 - search.proquest.com
In multivariate regression analysis, reduced-rank regression (RRR) has received
considerable attention as a powerful way of improving estimation and prediction …
considerable attention as a powerful way of improving estimation and prediction …