On-device research index

arXiv research

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.

169,181 papers · 148 categories

Trend · papers per month

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6 results for “genome-scale”

Bayesian Metabolic Flux Analysis reveals intracellular flux couplingsstat.ML

Bayesian analysis uncovers flux couplings in metabolic networks.

problem Uncertainty and unrealistic assumptions in traditional flux analysis methods.

method Introduces Bayesian metabolic flux analysis to model reactions probabilistically and infer flux distributions.

result Reveals informative flux couplings and more unobserved fluxes in metabolic networks.

Bayesian analysis metabolic flux analysis flux couplings genome-scale 13C flux measurements

2018-04-18abs ↗pdf ↗

BayReL: Bayesian Relational Learning for Multi-omics Data Integrationcs.LG

BayReL learns molecular interactions across multi-omics data.

problem Inferring meaningful interactions across diverse molecular data types.

method BayReL uses Bayesian representation learning with graph models to integrate multi-omics data.

result BayReL outperforms existing methods in inferring molecular interactions.

Bayesian Relational Learning multi-omics data integration molecular interactions graph models signal transduction

2020-10-12abs ↗pdf ↗

Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Networkq-bio.MN

Adjoint SA speeds up bioprocess parameter learning.

problem Challenges in digital twin development for biomanufacturing.

method Adjoint sensitivity analysis on multi-scale enzymatic reaction networks.

result Resilient sensitivities reveal bioprocess regulatory mechanisms.

adjoint sensitivity biomanufacturing stochastic reaction networks parameter learning digital twin

2024-05-07abs ↗pdf ↗

Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count datastat.ML

Bayesian model learns cancer subtypes from diverse NGS data.

problem Overdispersed NGS count data and limited samples for specific cancer types.

method Bayesian Multi-Domain Learning (BMDL) model using hierarchical negative binomial factorization.

result BMDL achieves reproducible cancer subtyping without negative transfer effects.

Bayesian learning multi-domain learning cancer subtype discovery next-generation sequencing overdispersed data

2018-10-22abs ↗pdf ↗

Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Datastat.ML

ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.

problem Causal inference in high-dimensional multi-omics data, especially when ignoring the hierarchical structure.

method Two-tiered divide-and-conquer strategy with ancestral conditioning sets.

result Achieves polynomial-time complexity and accurately recovers ancestral relationships.

causal discovery multi-omics hierarchical structure two-tiered polynomial-time

2026-07-05abs ↗pdf ↗

SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic dataq-bio.GN

SimCD simultaneously clusters cells and identifies differential gene expression in scRNA-seq data.

problem Separate clustering and differential expression analysis for scRNA-seq data leads to suboptimal results.

method Develops SimCD, a unified hierarchical gamma-negative binomial model for simultaneous cell clustering and differential expression analysis.

result SimCD outperforms existing methods in discovering cell clusters and capturing dynamic expression changes.

scRNA-seq cell clustering differential expression gamma-negative binomial dropout modeling

2021-04-04abs ↗pdf ↗