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.

169,291 papers · 148 categories

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4 results for “Osteoporosis”

A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discoverystat.ML

RobKMR improves robustness in multi-omics data analysis for osteoporosis biomarker discovery.

problem Sensitivity to adversarial outliers and lack of comprehensive multi-omics data integration.

method RobKMR, a non-linear M-estimator-based approach using robust kernel centered Gram matrix and robust score test.

result Selected biomarkers (DKK1, MTND5, FASTKD2) significantly bond with four drugs for osteoporosis.

robust kernel machine regression multi-omics data osteoporosis biomarker drug repurposing adversarial outliers

2022-01-13abs ↗pdf ↗

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networkscs.CV

CNNs improve automatic femur bone segmentation from MR images.

problem Manual segmentation of bone MR images is time-consuming and impractical.

method Deep convolutional neural networks (CNNs) trained on volumetric structural MR images of the proximal femur.

result CNNs achieved high segmentation accuracy (dice similarity score of 0.94

\pm

0.05).

CNNs MRI Bone Quality Osteoporosis Segmentation

2017-04-20abs ↗pdf ↗

Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small datasetstat.ML

The paper tackles Bayesian inference with small datasets using manifold learning.

problem Small datasets challenge Bayesian inference with non-Gaussian models.

method Manifold learning and sampling for Bayesian posterior approximation.

result The method effectively samples Bayesian posteriors with non-Gaussian models from small datasets.

Bayesian inference manifold learning small datasets non-Gaussian models manifold sampling

2019-10-28abs ↗pdf ↗

A generalized kernel machine approach to identify higher-order composite effects in multi-view datasetsstat.ML

Proposes a novel method to identify complex effects in multi-view datasets.

problem Challenges in analyzing multi-view biomedical datasets with complex interactions.

method Generalized kernel machine approach considering marginal and joint effects of features from different views.

result Effective identification of higher-order composite effects in multi-view datasets.

multi-view datasets higher-order effects kernel machine biomedical research complex diseases

2020-04-29abs ↗pdf ↗