Evidence map›Paper›PMID 30254965›Full record

ArticleWorld journal of clinical oncology2018

Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery.

Jeya Balaji Balasubramanian, Vanathi Gopalakrishnan

Abstract read
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In one paragraph

Article in World journal of clinical oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Jeya Balaji BalasubramanianIntelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15260, United States.
Vanathi GopalakrishnanDepartment of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206, United States. vanathi@pitt.edu.

Funding

Transfer Rule Learning with Functional Mapping for Integrative Modeling of Panomics DataR01GM100387 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GOPALAKRISHNAN, VANATHI · 2012 to 2018
$1.8M
NIGMS NIH HHS R01 GM100387
6 · The paper itself

Abstract

aimTo develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.

methodsBayesian rule learning (BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks (BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL

resultsWe evaluated the degree of incorporation of prior knowledge into BRL

conclusionBRL

Indexed as

Background knowledgeBayesian methodsBiomarker discoveryInformative priorsRule-based modelsSupervised machine learning

Identifiers

PMID30254965
PMCPMC6153126

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LicenceCC BY-NC
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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.