Evidence map›Paper›PMID 33241320›Full record

ArticleBioinformatics (Oxford, England)2021

COSIFER: a Python package for the consensus inference of molecular interaction networks.

Matteo Manica, Charlotte Bunne, Roland Mathis, Joris Cadow, Mehmet Eren Ahsen, Gustavo A Stolovitzky, María Rodríguez Martínez

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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  4. Identification ofFrontiers in immunology · 2024
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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

7 authors.

Matteo ManicaCognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.ORCID 0000-0002-8872-0269
Charlotte BunneCognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
Roland MathisCognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
Joris CadowCognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
Mehmet Eren AhsenDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA.
Gustavo A StolovitzkyDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA.
María Rodríguez MartínezCognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.

Funding

European Union's Horizon 2020 Research and Innovation Program 668858
6 · The paper itself

Abstract

summaryThe advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. AVAILABILITY AND IMPLEMENTATION: COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

SoftwareConsensus

Identifiers

PMID33241320
PMCPMC8337002

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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.