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.

168,657 papers · 148 categories

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8 results for “CRISPR”

Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+q-bio.QM

ContrastiveVI+ models CRISPR screens with noisy guide efficiency.

problem Noisy guide efficiency in CRISPR screens.

method Generative modeling framework that disentangles perturbation-induced from shared variations.

result ContrastiveVI+ better recovers perturbation-induced variations and identifies cells without edits.

CRISPR screens noisy guide efficiency perturbation-induced variations non-perturbation-related variations generative modeling

2024-11-11abs ↗pdf ↗

dotears: Scalable, consistent DAG estimation using observational and interventional datastat.ML

A new method infers causal gene regulatory networks from parallel CRISPR interventions and transcriptomic data.

problem Learning causal gene regulatory networks from observational data is complicated by lack of identifiability and a combinatorial solution space.

method A continuous optimization framework that leverages observational and interventional data to infer a single causal structure, assuming a linear Structural Equation Model (SEM).

result A provably consistent estimator of the true DAG under mild assumptions.

causal inference score-based structure learning structural equation model interventional data parallel CRISPR interventions

2023-05-30abs ↗pdf ↗

GeneDisco: A Benchmark for Experimental Design in Drug Discoverycs.LG

GeneDisco benchmarks experimental design for drug discovery.

problem Vast experimental design space in drug discovery.

method Machine learning for optimal experimental design.

result Standardised benchmark suite for active learning.

drug discovery experimental design active learning CRISPR machine learning

2021-10-22abs ↗pdf ↗

Causal Discovery under Off-Target Interventionscs.LG

This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.

problem Discovering the true causal graph from observational data with limited interventions.

method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.

result Provides approximation algorithms with competitive ratios for verification and search problems.

causal discovery stochastic interventions Markov equivalence approximation algorithms

2024-02-13abs ↗pdf ↗

Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomesstat.ME

A new method removes biases in data integration by using surrogate control outcomes.

problem Data integration methods can be biased due to data-dependent processes.

method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.

result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.

data integration post-integration inference surrogate control outcomes latent heterogeneity robust inference

2024-10-07abs ↗pdf ↗

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomesstat.ME

The paper develops methods for causal inference from single-cell RNA sequencing data with multiple outcomes.

problem Causal inference from single-cell RNA sequencing data with multiple heterogeneous outcomes.

method Generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes.

result Demonstrates the use of semiparametric inferential results for estimating causal effects in genomics.

Causal inference Single-cell RNA sequencing Multiple outcomes Doubly robust estimation Genomics

2024-04-14abs ↗pdf ↗

Deep Recurrent Neural Network for Protein Function Prediction from Sequenceq-bio.QM

As high-throughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by low-throughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary amino-acid sequences h…

2017-01-28abs ↗pdf ↗

Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventionscs.LG

New method for selective prediction under interventions learns causal structure from data.

problem Tight uncertainty sets in selective conformal prediction under unknown interventional settings.

method Partial causal structure learning for descendant indicators, contamination-robust coverage theorem, algorithms for descendant discovery and distance estimation.

result Valid selective conformal prediction under contamination up to 30% with controlled coverage.

selective prediction interventional settings contamination robustness causal structure learning distance estimation

2026-03-02abs ↗pdf ↗