Evidence map›Paper›PMID 37450113›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2023

The Interactome of Protein, DNA, and RNA.

Yuyao Yan, Tao Huang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

4 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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

2 authors at 2 institutions in 1 country.

Yuyao YanBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Tao HuangCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China. huangtao@sinh.ac.cn.
Chinese Academy of Sciences · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteins participate in many processes of the organism and are very important for maintaining the health of the organism. However, proteins cannot function independently in the body. They must interact with proteins, DNA, RNA, and other substances to perform biological functions and maintain the body's health. At present, there are many experimental methods and software tools that can detect and predict the interaction between proteins and other substances. There are also many databases that record the interaction between proteins and other substances. This article mainly describes protein-protein, protein-DNA, and protein-RNA interactions in detail by introducing some commonly used experimental methods, the software tools produced with the accumulation of experimental data and the rapid development of machine learning, and the related databases that record the relationship between proteins and some substances. By this review, we hope that through the analysis and summary of various aspects, it will be convenient for researchers to conduct further research on protein interactions.

Indexed as

ProteinsRNADNAElectrophoretic Mobility Shift AssaySoftwareDNAProteinsRNAChIPCo-immunoprecipitation (Co-IP)Deep LearningElectrophoretic Mobility Shift Assay (EMSA)EuRBPDBNetwork AnalysisProtein Data Bank (PDB)Protein–DNA InteractionProtein–Protein InteractionProtein–RNA InteractionProximity LabelingSTRINGYeast Two-Hybrid

Identifiers

PMID37450113
OpenAlexW4384347126

What OpenQuestion holds

Textmetadata
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.