Evidence map›Paper›PMID 27832067›Full record

ArticlePLoS computational biology2016

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.

Lefteris Koumakis, Alexandros Kanterakis, Evgenia Kartsaki, Maria Chatzimina, Michalis Zervakis, Manolis Tsiknakis, Despoina Vassou, Dimitris Kafetzopoulos, Kostas Marias, Vassilis Moustakis and 1 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Two gene set variation indexes as potential diagnostic tool for sepsis.American journal of translational research · 2020
    Article
  6. Deep learning models in genomics; are we there yet?Computational and structural biotechnology journal · 2020
    Review
  7. Review
  8. Article
  9. Article
  10. 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

11 authors.

Lefteris KoumakisComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.
Alexandros KanterakisComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.ORCID http://orcid.org/0000-0003-4276-0115
Evgenia KartsakiComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.
Maria ChatziminaComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.
Michalis ZervakisSchool of Electrical and Computer Engineering, Technical University of Crete, Greece.
Manolis TsiknakisComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.ORCID http://orcid.org/0000-0001-8454-1450
Despoina VassouInstitute of Molecular Biology & Biotechnology, FORTH, Heraklion, Crete, Greece.
Dimitris KafetzopoulosInstitute of Molecular Biology & Biotechnology, FORTH, Heraklion, Crete, Greece.ORCID http://orcid.org/0000-0002-8086-0371
Kostas MariasComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.
Vassilis MoustakisSchool of Production Engineering & Management, Technical University of Crete, Greece.
George PotamiasComputational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Indexed as

Databases, GeneticModels, BiologicalAlgorithmsComputer SimulationData MiningGene Expression ProfilingProteomeSignal TransductionSoftwareProteome

Identifiers

PMID27832067
PMCPMC5104320

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.