ReviewBioMed research international2014
Systems biology in the context of big data and networks.
Review in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Tensor network-based gene regulatory network inference for single-cell transcriptomic data.iScience · 2026Article
- The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.Biology · 2026Review
- Python-Based Interactive Notebooks for Teaching AI and Data Science to Undergraduate Students in the Life Sciences.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Multi-omics analysis reveals discordant proteome and transcriptome responses in larval guts of Frankliniella occidentalis infected with an orthotospovirus.Insect molecular biology · 2025Article
- Identification and validation of hub genes associated with biotic and abiotic stresses by modular gene co-expression analysis in Oryza sativa L.Scientific reports · 2025Article
- Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Article
- Network model integrated with multi-omic data predicts MBNL1 signals that drive myofibroblast activation.iScience · 2023Article
- Editorial: Expert Opinions in Network bioinformatics: 2022.Frontiers in bioinformatics · 2023Article
- The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.Scientific reports · 2022Article
- Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm.Plant methods · 2022Article
- A roadmap for the functional annotation of protein families: a community perspective.Database : the journal of biological databases and curation · 2022Article
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Review and assessment of Boolean approaches for inference of gene regulatory networks.Heliyon · 2022Review
- LinkedImm: a linked data graph database for integrating immunological data.BMC bioinformatics · 2021Article
- Panomics: New Databases for Advancing Cardiology.Frontiers in cardiovascular medicine · 2021Review
- Translational Informatics for Natural Products as Antidepressant Agents.Frontiers in cell and developmental biology · 2021Review
- Big data in systemic sclerosis: Great potential for the future.Journal of scleroderma and related disorders · 2020Review
- Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control.Communicative & integrative biology · 2020Article
- Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches.Frontiers in neuroinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.
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.