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.
Elastic co-clustering improves clustering of single-cell genomic data.
GENOT matches cells across data modalities using neural OT solvers.
The paper develops methods for causal inference from single-cell RNA sequencing data with multiple outcomes.
MarkerMap selects key genes for cell type analysis in single-cell RNA-seq.
The paper improves Fisher-Pitman tests for Poisson mixtures, detecting autism-related genes.
scICML integrates multi-omics data from single cells using co-clustering.
The paper proposes a method to infer differentiation trees from RNA velocity data.
New method improves clustering accuracy in noisy single-cell data.
We present a Bayesian hierarchical multi-view mixture model termed Symphony that simultaneously learns clusters of cells representing cell types and their underlying gene regulatory networks by integrating data from two views: single-cell gene expression data and paired epigenetic data, which is informative of gene-gen…
Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from bulk transcriptomic data may therefore not be adapted to scRNA-seq data…
Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experim…
Paper develops statistical tests for covariance matrix regression on manifold.
SimCD simultaneously clusters cells and identifies differential gene expression in scRNA-seq data.
PolyILR: A Tree-Structured Orthonormal Decomposition of Compositional Data
With the wealth of high-throughput sequencing data generated by recent large-scale consortia, predictive gene expression modelling has become an important tool for integrative analysis of transcriptomic and epigenetic data. However, sequencing data-sets are characteristically large, and previously modelling frameworks …
Federated learning improves bioinformatics by sharing data legally.
New method learns complex cell networks from millions of cells.
Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new view…
Kernel testing compares cell states in single-cell data.
Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this rev…
In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene exp…
Improved GPLVM model for single-cell RNA-seq data.
Paper proposes dp-VAE for preserving spatial context in gene expression data.
Forest Fire Clustering discovers cell types from single-cell data.
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
NESS improves neighbor embedding for smooth cell-state transitions in single-cell data.
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order in…
SMAI framework tests and integrates single-cell data alignability.
BasisVAE combines VAE and clustering for tabular data analysis.
GAGA learns a warped metric for geometry-aware data generation and interpolation.
With ongoing developments and innovations in single-cell RNA sequencing methods, advancements in sequencing performance could empower significant discoveries as well as new emerging possibilities to address biological and medical investigations. In the study, we will be using the dataset collected by the authors of Sys…
HSSE framework embeds single-cell RNA-seq data at multiple scales.
BanditPAM clusters data faster than traditional methods.
With different genomes available, unsupervised learning algorithms are essential in learning genome-wide biological insights. Especially, the functional characterization of different genomes is essential for us to understand lives. In this book chapter, we review the state-of-the-art unsupervised learning algorithms fo…
New model generates realistic single-cell gene expression data.
New model identifies cell-specific genes for cancer prognosis.
Graph Attention Networks predict disease state from single-cell data.
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this manuscript, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to accurately predicting 1). the ranking positions of sensitive drugs an…
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However,…
Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measu…
Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into accou…
Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profil…
New method learns cell trajectories from multiple snapshots.
We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an "overall" mode of somatic mutational noise. We give a prescription …
New method handles correlated genes for better genomic prediction.
ChemCPA predicts cellular responses to novel drugs using transfer learning.
Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct coun…
GROOVE learns representations for weakly paired multimodal data.