Evidence map›Paper›PMID 33313341›Full record

ArticleIEEE transactions on molecular, biological, and multi-scale communications2018

Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer (Invited Paper).

Nitin Singh, Mehmet Eren Ahsen, Niharika Challapalli, Hyun-Seok Kim, Michael A White, M Vidyasagar

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on molecular, biological, and multi-scale communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 41% 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, 8 citations in OpenAlex.

  1. Review
  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

6 authors at 3 institutions in 3 countries.

Nitin SinghDepartment the Department of Bioengineering at the University of Texas at Dallas.
Mehmet Eren AhsenGies College of Business, Champaign, IL.
Niharika ChallapalliElectrical Engineering Department at the University of Texas at Dallas, Richardson, TX.
Hyun-Seok KimSeverance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea.
Michael A WhitePfizer, La Jolla, CA, on leave from the UT Southwestern Medical Center, Dallas.
M VidyasagarSystems Engineering Department at the University of Texas at Dallas, Richardson, TX. He is now with the Indian Institute of Technology, Hyderabad.
The University of Texas at Dallas · USSouthwestern Medical Center · USYonsei University · KR

Funding

Lung cancer oncogenotype-selective drug target discoveryU01CA176284 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI MACMILLAN, JOHN B, MINNA, JOHN D. · 2013 to 2016
$2.8M
Targeting Mechanistic Subtypes of Neoplastic DiseaseR35CA197717 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI WHITE, MICHAEL A. · 2015 to 2015
$725k
NCI NIH HHS R35 CA197717NCI NIH HHS U01 CA176284
6 · The paper itself

Abstract

Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper we propose a new algorithm, based on a concept from probability theory known as the

Identifiers

PMID33313341
PMCPMC7731978
OpenAlexW2966172427

What OpenQuestion holds

Textmetadata
LicenceTDM
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.