ReviewAdvances in experimental medicine and biology2014
Systems analysis of high-throughput data.
Review in Advances in experimental medicine and biology, 2014. 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, 30 citations in OpenAlex.
- Computational Analysis of Balanites aegyptiaca Phytochemicals as Inhibitors of Human Pancreatic α-Amylase.Chemistry & biodiversity · 2026Article
- Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives.Molecular metabolism · 2022Review
- Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis.Life (Basel, Switzerland) · 2022Review
- Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities.Journal of genetics and genomics = Yi chuan xue bao · 2021Review
- Genomic and Genetic Approaches to Deciphering Acute Respiratory Distress Syndrome Risk and Mortality.Antioxidants & redox signaling · 2019Review
- Recent Advances in Design of Fluorescence-Based Assays for High-Throughput Screening.Analytical chemistry · 2019Review
- Internal driving factors leading to extrahepatic manifestation of the hepatitis C virus infection.International journal of molecular medicine · 2017Article
- Reorganization of gene network for degradation of polycyclic aromatic hydrocarbons (PAHs) in Pseudomonas aeruginosa PAO1 under several conditions.Journal of applied genetics · 2017Article
- SOScientific reports · 2017Article
- Viral Phylogenomics Using an Alignment-Free Method: A Three-Step Approach to Determine Optimal Length of k-mer.Scientific reports · 2017Article
- Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China.Scientific reports · 2016Article
- Animal-Free Chemical Safety Assessment.Frontiers in pharmacology · 2016Review
- Computational Tools for Parsimony Phylogenetic Analysis of Omics Data.Omics : a journal of integrative biology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.
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.