Evidence map›Paper›PMID 30178538›Full record

ArticleImmunology2018

Bioinformatics for immunologists.

Daniel M Altmann

Open access · bronzeAbstract readEditorial
In one paragraph

Article in Immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 92% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

1 author at 1 institution in 1 country.

Daniel M AltmannDepartment of Medicine, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK.
Hammersmith Hospital · GB

Funding

Versus Arthritis 21135
6 · The paper itself

Abstract

Immunology was once a specialty prone to cause dismay or even scepticism among outsiders for its struggles to visualize poorly understood, complex interactions through descriptive models integrating cell types, their factors and functions. This was the age of 'too many soft ideas propped up by too little hard data'. Twenty-first century immunologists have the advantage of being able to marry this rich conceptual legacy to a contemporary toolkit offering such depth of hard data across different 'omics' platforms, that they are faced by the opposite dilemma: 'too much hard data to comprehend or synthesize into a meaningful narrative'. Approaches including next-generation sequencing of host and pathogen genomes and transcriptomes, metagenomics of the microbiota, creative strategies for receptor repertoire sequencing, and then for proteomics and metabolomics, encompass all that is needed to tell the entire story, if only we are creative enough, not only to evaluate the message from any given omics platform, but to derive the tools that enable us to integrate the answers from diverse omics platforms in a meaningful way. To achieve this goal, there is an urgent need to ensure we train the next generation of bioinformatically literate researchers.

Indexed as

Allergy and ImmunologyComputational BiologyHigh-Throughput Nucleotide SequencingHumans

Identifiers

PMID30178538
PMCPMC6099173
OpenAlexW2888742867

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.