ArticleGigaScience2021
Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.PLoS computational biology · 2024Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.bioRxiv : the preprint server for biology · 2024Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- Article
- Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.PLoS computational biology · 2021Article
- Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.Metabolites · 2021Review
- Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data.NAR genomics and bioinformatics · 2021Article
- Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.GigaScience · 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
6 authors.
Funding
Abstract
backgroundpreviously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software.
resultshere we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning-based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression. CONCULSION: Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.
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.