ReviewBriefings in bioinformatics2026
Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.
Review in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Over the past 75 years, especially the recent quarter century, bioinformatics has undergone a profound transformation, evolving from a specialized field of command-line tools to a cornerstone of modern biomedical and life sciences. We trace this journey through distinct technological eras driven by exponential biological data growth and parallel computational advances. The genomic revolution established foundational sequence analysis tools and was rapidly followed by the next-generation sequencing era, when unprecedented data volumes shifted the bottleneck from generation to analysis. This drove the development of web servers, cloud platforms, and containerized workflows to address scalability, accessibility, and reproducibility challenges. We now stand in the artificial intelligence (AI)-driven era, where deep learning (e.g. AlphaFold series) and large language models reshape structural biology, multi-modal data integration, and how researchers interact with tools through natural language prompting. This review highlights a recurring pattern as each technological era lowers barriers to entry, it simultaneously introduces new questions about transparency, trust, and rigor. By framing the popular rise of AI within this historical context, we provide a critical roadmap for navigating the cultural, ethical, and technical crossroads facing the next generation of bioinformatics.
Indexed as
Identifiers
What Socratic 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 Socratic graph.