ReviewMolecular diagnosis & therapy2017
Mining Cancer Transcriptomes: Bioinformatic Tools and the Remaining Challenges.
Review in Molecular diagnosis & therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- ANXA6/TRPV2 axis promotes lymphatic metastasis in head and neck squamous cell carcinoma by inducing autophagy.Experimental hematology & oncology · 2023Article
- Analysis of mScientific reports · 2022Article
- Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer.Journal of Cancer · 2021Article
- Gene Expression Profiles Identified Novel Urine Biomarkers for Diagnosis and Prognosis of High-Grade Bladder Urothelial Carcinoma.Frontiers in oncology · 2020Article
- A novel role of Krüppel-like factor 8 as an apoptosis repressor in hepatocellular carcinoma.Cancer cell international · 2020Article
- Role of COL3A1 and POSTN on Pathologic Stages of Esophageal Cancer.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of next-generation sequencing technologies has had a profound impact on the field of cancer genomics. With the enormous quantities of data being generated from tumor samples, researchers have had to rapidly adapt tools or develop new ones to analyse the raw data to maximize its value. While much of this effort has been focused on improving specific algorithms to get faster and more precise results, the accessibility of the final data for the research community remains a significant problem. Large amounts of data exist but are not easily available to researchers who lack the resources and experience to download and reanalyze them. In this article, we focus on RNA-seq analysis in the context of cancer genomics and discuss the bioinformatic tools available to explore these data. We also highlight the importance of developing new and more intuitive tools to provide easier access to public data and discuss the related issues of data sharing and patient privacy.
Indexed as
Identifiers
28229366What 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.