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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4 results for “bortezomib-induced neuropathy”

Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studiesstat.ME

Method clusters SNPs to improve detection of disease-associated variants.

problem Over-conservative SNP-wise approach in GWASs.

method Model-based clustering of SNPs into three clusters, borrowing information across SNPs.

result Proposed method outperforms traditional SNP-wise approach in controlling false discovery rate and sensitivity.

GWAS SNP clustering disease association multiple myeloma bortezomib-induced neuropathy

2018-06-21abs ↗pdf ↗

Learning the Parameters of Determinantal Point Process Kernelsstat.ML

Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing properties, such as efficient sampling, learning the parameters of a DPP is still considered a difficult problem due to the non-convex nature…

2014-02-20abs ↗pdf ↗

Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Samplingstat.ML

Proposes SSL method for non-randomly sampled data.

problem Evaluation of prediction rules under non-random sampling.

method Two-step procedure with imputation and augmentation.

result Proposed method outperforms supervised methods in efficiency.

SSL non-random sampling imputation stratified sampling prediction rules

2020-10-19abs ↗pdf ↗

Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Healthq-bio.QM

AI identifies patient clusters for diabetes case management.

problem Diabetes complications and mental health comorbidities drive high healthcare costs.

method Combined AI techniques with diverse data sources for prediction and clustering.

result 83.5% accuracy in predicting diabetes complications and meaningful patient clusters.

Artificial Intelligence Diabetes Management Complications Prediction Mental Health Case Management

2018-10-06abs ↗pdf ↗