ArticleBioanalysis2015
Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.
Article in Bioanalysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Machine Learning for Enhanced Identification Probability in RPLC/HRMS Nontargeted Workflows.Analytical chemistry · 2025Article
- Application of artificial intelligence to quantitative structure-retention relationship calculations in chromatography.Journal of pharmaceutical analysis · 2025Review
- Review
- A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.Engineering in life sciences · 2024Article
- Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data.Journal of cheminformatics · 2023Article
- A pilot study of ion current estimation by ANN from action potential waveforms.Journal of biological physics · 2022Article
- The application of artificial neural networks in metabolomics: a historical perspective.Metabolomics : Official journal of the Metabolomic Society · 2019Review
- Challenges in Identifying the Dark Molecules of Life.Annual review of analytical chemistry (Palo Alto, Calif.) · 2019Review
- Review
- Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.Analytical chemistry · 2018Article
- Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds.Journal of chemical information and modeling · 2018Article
- Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.Metabolites · 2018Review
- Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification.Drug metabolism letters · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundArtificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization. METHODOLOGY: We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds. Models were assessed by independent validation (I-Val) using newly measured RI values for 1492 compounds.
conclusionThe best model demonstrated an I-Val standard error of 55 RI units and was built using a Ward's clustering data split and a minimally nonlinear network architecture. Use of validation statistics for stopping and final model selection resulted in better independent validation performance than the use of test set statistics.
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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.