Evidence map›Paper›PMID 27282231›Full record

ReviewComputer methods and programs in biomedicine2016

A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles.

David E Jones, Hamidreza Ghandehari, Julio C Facelli

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Computer methods and programs in biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

3 authors.

David E JonesDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA.
Hamidreza GhandehariDepartments of Bioengineering and Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112, USA; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA.
Julio C FacelliDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: julio.facelli@utah.edu.

Funding

UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
University of Utah Center for Clinical and Translational ScienceUL1TR001067 · NCATS · UNIVERSITY OF UTAH · PI DERE, WILLARD H., HESS, RACHEL · 2013 to 2017
$19.8M
BIOLOGICAL FATE AND BIOCOMPATIBILITY OF SILICA-BASED NANOCONSTRUCTSR01ES024681 · NIEHS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Hamid Ghandehari · 2014 to 2026
$4.1M
NCATS NIH HHS UL1 TR001067NIEHS NIH HHS R01 ES024681NLM NIH HHS T15 LM007124
6 · The paper itself

Abstract

This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design.

Indexed as

Data MiningMachine LearningNanoparticlesAnimalsCell LineHumansData miningMachine learningNanoinformaticsNanomedicine

Identifiers

PMID27282231
PMCPMC4902872

What OpenQuestion holds

Textmetadata
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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.