ArticleImmunology2018
Bioinformatics for immunologists.
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.
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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Investigation of molecular immune regulation mechanisms and therapeutic target prediction in pancreatic cancer based on bioinformatics and genetics.Clinics (Sao Paulo, Brazil) · 2026Article
- Comprehensive analysis and validation of angiogenesis in vascular dementia from the perspective of diagnosis, prevention, and treatment.Frontiers in genetics · 2025Article
- Unveiling the role of miR-137-3p/miR-296-5p/SERPINA3 signaling in colorectal cancer progression: integrative analysis of gene expression profiles and in vitro studies.BMC medical genomics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.