ReviewFrontiers in genetics2013
On protocols and measures for the validation of supervised methods for the inference of biological networks.
Review in Frontiers in genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Nitrogen sensing and regulatory networks: it's about time and space.The Plant cell · 2024Review
- Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains.PLoS computational biology · 2024Article
- Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase.Scientific reports · 2023Article
- Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients.International journal of general medicine · 2023Article
- Article
- Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection.The Laryngoscope · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- Challenges and opportunities in network-based solutions for biological questions.Briefings in bioinformatics · 2022Article
- Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.Pharmaceutics · 2021Article
- FARCI: Fast and Robust Connectome Inference.Brain sciences · 2021Article
- Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria.Pharmaceutics · 2021Article
- Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.Annual review of plant biology · 2021Review
- Cold-Start Problems in Data-Driven Prediction of Drug-Drug Interaction Effects.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Network inference in systems biology: recent developments, challenges, and applications.Current opinion in biotechnology · 2020Review
- Prediction of the miRNA interactome - Established methods and upcoming perspectives.Computational and structural biotechnology journal · 2020Review
- Network inference with ensembles of bi-clustering trees.BMC bioinformatics · 2019Article
- Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks.Scientific reports · 2018Article
- Integration of omic networks in a developmental atlas of maize.Science (New York, N.Y.) · 2016Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature. In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.
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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.