ArticleBriefings in bioinformatics2022
Machine learning alternative to systems biology should not solely depend on data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 19 citations in OpenAlex.
- Applying a Pharmacometrics-Enabled Machine Learning Analysis to Predict 2-Month Culture Conversion Using Phase 2a Data in a Tuberculosis Clinical Trial.Clinical and translational science · 2026Trial
- Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.Pigment cell & melanoma research · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Zero-shot prediction of drug responses using biologically informed neural networks trained on phosphoproteomic timeseries.PLoS computational biology · 2026Article
- Are we ready for causal discovery in biological systems using deep learning?Briefings in bioinformatics · 2026Article
- Systems biology in the era of AI: "winter" or "evolution"?Frontiers in systems biology · 2026Article
- Network biology to artificial intelligence: building the next generation of predictive drug discovery.Frontiers in immunology · 2026Review
- Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.NPJ systems biology and applications · 2024Article
- Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises.BMC biology · 2024Article
- Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli.NPJ systems biology and applications · 2024Article
- A Reinforcement Learning approach to study climbing plant behaviour.Scientific reports · 2024Article
- Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: itsFrontiers in systems biology · 2024Article
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 at 1 institution in 1 country.
Funding
Abstract
In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar 'AI winters' that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.
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.