ReviewInternational journal of genomics2017
A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries.
Review in International journal of genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- A guided network estimation approach using multi-omic information.BMC bioinformatics · 2024Article
- OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: implication to breeding.Animal biotechnology · 2023Review
- Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Multi-Omics Advancements towardsDiagnostics (Basel, Switzerland) · 2021Review
- Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study.JMIR medical education · 2021Article
- Mining Proteome Research Reports: A Bird's Eye View.Proteomes · 2021Article
- Review
- An Improved Stacked Autoencoder for Metabolomic Data Classification.Computational intelligence and neuroscience · 2021Article
- OmixLitMiner: A Bioinformatics Tool for Prioritizing Biological Leads from 'Omics Data Using Literature Retrieval and Data Mining.International journal of molecular sciences · 2020Article
- Transcriptomic data-driven discovery of global regulatory features of rice seeds developing under heat stress.Computational and structural biotechnology journal · 2020Article
- Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy.Journal of translational medicine · 2019Article
- Progress in data interoperability to support computational toxicology and chemical safety evaluation.Toxicology and applied pharmacology · 2019Review
- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Review
- The High-Throughput Analyses Era: Are We Ready for the Data Struggle?High-throughput · 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
No grant is acknowledged in the PubMed record.
Abstract
In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.
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.