ReviewPLoS computational biology2017
A review of active learning approaches to experimental design for uncovering biological networks.
Review in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- Deep active learning-based experimental design to uncover synergistic genetic interactions for host-targeted therapeutics.Bioinformatics advances · 2026Article
- Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions.Nanomaterials (Basel, Switzerland) · 2025Review
- Article
- Marker selection strategies for circulating tumor DNA guided by phylogenetic inference.Bioinformatics (Oxford, England) · 2025Article
- Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.Research square · 2024Article
- Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.bioRxiv : the preprint server for biology · 2024Article
- Predicting potential target genes in molecular biology experiments using machine learning and multifaceted data sources.iScience · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico.Development, growth & differentiation · 2023Review
- RECOVER identifies synergistic drug combinations in vitro through sequential model optimization.Cell reports methods · 2023Article
- High-throughput biochemistry in RNA sequence space: predicting structure and function.Nature reviews. Genetics · 2023Review
- Active learning applied to automated physical systems increases the rate of discovery.Scientific reports · 2023Article
- Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022Article
- Utilizing graph machine learning within drug discovery and development.Briefings in bioinformatics · 2021Article
- Regression plane concept for analysing continuous cellular processes with machine learning.Nature communications · 2021Article
- Network analyses in microbiome based on high-throughput multi-omics data.Briefings in bioinformatics · 2021Review
- Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae).PLoS neglected tropical diseases · 2021Article
- Optimal experimental design for mathematical models of haematopoiesis.Journal of the Royal Society, Interface · 2021Article
- Deep Learning for Protein-Protein Interaction Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2021Article
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
Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area.
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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.