miRmap: Comprehensive prediction of microRNA target repression strength
Charles E. Vejnar; Evgeny M. Zdobnov · 2012 · Nucleic Acids Research
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA 'seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We dev
Abstract by Charles E. Vejnar; Evgeny M. Zdobnov, Nucleic Acids Research (2012) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Power and Predictive Accuracy of Polygenic Risk Scores
Negative / Null Result ReportLocoregional Recurrence After Sentinel Lymph Node Dissection With or Without Axillary Dissection in Patients With Sentinel Lymph Node Metastases
Negative / Null Result ReportCritical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide
Negative / Null Result ReportAlpelisib plus fulvestrant for PIK3CA-mutated, hormone receptor-positive, human epidermal growth factor receptor-2–negative advanced breast cancer: final overall survival results from SOLAR-1
Negative / Null Result ReportA randomized placebo-controlled trial of idebenone in Leber’s hereditary optic neuropathy
Negative / Null Result ReportThe CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1093/nar/gks901