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.
A clinical Meta-Dataset from TCGA for multi-task learning.
We developed OmicsMapNet approach to take advantage of existing deep leaning frameworks to analyze high-dimensional omics data as 2-dimensional images. The omics data of individual samples were first rearranged into 2D images in which molecular features related in functions, ontologies, or other relationships were orga…
A new method STMF improves missing value prediction using tropical semiring.
Proposes a copula-based model for multi-view clustering with directional dependency.
Paper identifies key CpG methylation sites for breast cancer.
Deep learning improves tumor type classification accuracy.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
A new framework for PPLS combines noise estimation, optimization, and calibration.
Paper proposes scalable method for analyzing multi-omic data.
We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD…
Unified framework for robust causal directionality in quantum systems under MNAR observation.
Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology. However, multi-omic data has the "big p, small N" problem (the number of features is large, but the number of samples is small), it is challenging…
Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. Ho…
We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to non-uniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat…
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Se…
New method quantifies feature interactions in machine learning models.
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive …
The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…
Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introd…
CN-SBM clusters cancer samples and regions based on copy number variants.
A new method for federated survival analysis using Cox models.
MEM learns set functions from permutation-invariant data.
Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different …
Standard models assign disease progression to discrete categories or stages based on well-characterized clinical markers. However, such a system is potentially at odds with our understanding of the underlying biology, which in highly complex systems may support a (near-)continuous evolution of disease from inception to…
VICatMix clusters categorical biomedical data efficiently and selects relevant variables.
Modeling correlated mutations in cancer for personalized treatment.
Gene expression data represents a unique challenge in predictive model building, because of the small number of samples compared to the huge amount of features . This "" property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating ex…
A new method combines multiple cancer datasets to improve analysis.
New methods integrate nonlinear, sparse, and multi-view aspects for high-dimensional data analysis.
BIDIFAC+ factorizes linked matrices for cancer studies.
Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of variou…
Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.
While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of …
Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into co…
A new knockoff statistic using conditional prediction function improves variable selection in complex models.
Generative models often fail to preserve joint structure despite matching marginals.
Omics-GAN uses GANs to generate synthetic multi-omics data for improved disease prediction.
Due to advances in sensors, growing large and complex medical image data have the ability to visualize the pathological change in the cellular or even the molecular level or anatomical changes in tissues and organs. As a consequence, the medical images have the potential to enhance diagnosis of disease, prediction of c…
GPCCA integrates multi-modal data with missing values, improving clustering accuracy.