A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep Learning model diagnoses four lymphoma categories with high accuracy.
Study uses ML to predict HL survival, outperforming CoxPH.
Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined…
New method links covariates to CTMCs using RKHS, improving state transitions modeling.
A hybrid method clusters and characterizes cancer data efficiently.
The estimation of covariance matrices of gene expressions has many applications in cancer systems biology. Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies. Motivated by the traditional me…
Proposes a fusion method for many treatment groups in ITRs.
We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network s…
Improves convex biclustering for high-dimensional data.
Extends deep learning for nonlinear Cox regression variable selection.
We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An -penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other -penalized estimators as specia…
Proposes using external data to improve predictions in medical applications with limited samples.