Evidence map›Paper›PMID 37102071›Full record

ArticleStatistica Sinica2023

Feature-weighted elastic net: using "features of features" for better prediction.

J Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani

Abstract read
In one paragraph

Article in Statistica Sinica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Penalized regression with multiple sources of prior effects.Bioinformatics (Oxford, England) · 2023
    Article
  5. Article
  6. Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023
    Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

J Kenneth TayDepartment of Statistics, Stanford University.
Nima AghaeepourDepartment of Anesthesiology, Pain, and Perioperative Medicine, Stanford University.
Trevor HastieDepartment of Statistics, Stanford University.
Robert TibshiraniDepartment of Statistics, Stanford University.

Funding

Institutional Career Development Core (KL2)KL2TR003143 · NCATS · STANFORD UNIVERSITY · PI ASCH, STEVEN M. · 2019 to 2023
$8.7M
New Statistical Methods for Medical Signals and ImagesR01EB001988 · NIBIB · STANFORD UNIVERSITY · PI JOHNSTONE, IAIN M · 2003 to 2018
$6.1M
SPRINT: Signature for Pain Recovery IN TeensR61NS114926 · NINDS · STANFORD UNIVERSITY · PI SIMONS, LAURA E · 2019 to 2019
$5.4M
Dietary Modulation of Neuroinflammation in Age-Related Memory DisordersR01AG058417 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SLOAN, RICHARD P, SMALL, SCOTT A · 2018 to 2022
$4.2M
New Statistical Methods for Medical Signals and ImagesR01GM134483 · NIGMS · STANFORD UNIVERSITY · PI Iain M Johnstone · 2019 to 2026
$3.9M
Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
NCATS NIH HHS KL2 TR003143NIA NIH HHS R01 AG058417NIBIB NIH HHS R01 EB001988NIGMS NIH HHS R01 GM134483NIGMS NIH HHS R35 GM138353NINDS NIH HHS R61 NS114926
6 · The paper itself

Abstract

In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the

Identifiers

PMID37102071
PMCPMC10129060

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.