Evidence map›Paper›PMID 27462364›Full record

ArticleComputational and mathematical methods in medicine2016

A Run-Length Encoding Approach for Path Analysis of C. elegans Search Behavior.

Li Huang, Hongkyun Kim, Jacob Furst, Daniela Raicu

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% of its field
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. 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 at 2 institutions in 1 country.

Li HuangSchool of Computing, College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA.ORCID 0000-0002-9681-2606
Hongkyun KimDepartment of Cell Biology and Anatomy, Chicago Medical School, Rosalind Franklin University, North Chicago, IL 60064-3095, USA.
Jacob FurstSchool of Computing, College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA.
Daniela RaicuSchool of Computing, College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA.ORCID 0000-0002-2165-7234
DePaul University · USRosalind Franklin University of Medicine and Science · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The nematode Caenorhabditis elegans explores the environment using a combination of different movement patterns, which include straight movement, reversal, and turns. We propose to quantify C. elegans movement behavior using a computer vision approach based on run-length encoding of step-length data. In this approach, the path of C. elegans is encoded as a string of characters, where each character represents a path segment of a specific type of movement. With these encoded string data, we perform k-means cluster analysis to distinguish movement behaviors resulting from different genotypes and food availability. We found that shallow and sharp turns are the most critical factors in distinguishing the differences among the movement behaviors. To validate our approach, we examined the movement behavior of tph-1 mutants that lack an enzyme responsible for serotonin biosynthesis. A k-means cluster analysis with the path string-encoded data showed that tph-1 movement behavior on food is similar to that of wild-type animals off food. We suggest that this run-length encoding approach is applicable to trajectory data in animal or human mobility data.

Indexed as

Appetitive BehaviorBehavior, AnimalAlgorithmsAnimalsCaenorhabditis elegansCluster AnalysisComputational BiologyFeeding BehaviorGenotypeMachine LearningMovementPattern Recognition, AutomatedSoftware

Identifiers

PMID27462364
PMCPMC4944090
OpenAlexW2461828133

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.