v1.0
Scores rank candidate interactions. Evidence shows what the model relied on, not a verified mechanism. Paper value from the paperExport model output, mk_ns_nodrop, 19 Oct 2026ViennaRNA structure computed with ViennaRNA 2.7.2, display only
miRNA–gene interaction atlas

miRNA target predictions you can audit.

MechKG scores every pair of 883 miRNAs and 3,303 genes, and splits each score into the protein-interaction, pathway, disease and sequence evidence behind it.

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Formats: miR-34a-5p MIMAT0000255 HSPA1B ENSG00000204388 miR-34a-5p × HSPA1B

Scores rank candidates for experiments. Evidence shows what the model relied on, not a verified mechanism.

Score ledger
hsa-miR-34a-5p × HSPA1B
Running total
Mean of the five models. Each line is one piece of evidence, and the lines add up exactly to the score.
883miRNAs
3,303protein-coding genes
10,112validated targeting relations
22,456graph entities
200,224graph relations

Counts from the MechKG paper. Sources: miRTarBase, miRATBase, MSigDB, GO, HMDD, CTD and STRING.

How a score is built

Three steps, and nothing hidden between them

01

Read the knowledge graph

22,456 entities joined by nine relation types: validated targeting, pathway and GO membership, disease association and protein interaction.

02

Collect evidence for the pair

Three graph channels gather specific paths from the miRNA's known targets to the candidate gene. A separate channel counts seed sites in the 3′UTR.

03

Add it up in a ledger

The score is an exact sum of per-evidence contributions, channel background terms and the sequence term. Removing one line changes the score by exactly that line.

Built to be checked

Every claim on this site has a test behind it

95%

Faithful explanations

Deleting the edges behind the top evidence lowers the score more than deleting a matched random edge in 95% of tested samples.

Paper
5 / 5

Gains beyond popularity

On the degree-matched test set, all five split models improve on HGT with the same encoder, by 2.8 points of AUC on average.

Paper
14

Independent wet-lab data

In 16 transfection datasets, scores track down-regulation for 14 miRNAs after controlling for sequence features and gene popularity.

Paper
137

Audited labels

Validated records that likely carry the wrong miRNA arm are flagged, with site and CLASH support, so curators can review them.

Paper
Featured record

hsa-miR-34a-5p × HSPA1B

A held-out candidate: no miR-34a pair of this gene was in any training or test split.

Rank
3rd among held-out candidates; top 20 in all five models
Overexpression
log2 fold change −0.44, FDR 4×10⁻⁵ (HEK293T, GSE274168)
Direct binding
miR-34a chimeric reads in HEK293T CLASH data
Sequence
One 8mer site, 3′UTR positions 242–249
Graph evidence
GO: centrosome, 10.4 known targets against 6.1 expected
Paper
Evaluation

Tested with popularity removed

Random negatives reward models that learn which miRNAs and genes are well studied. The degree-matched test removes that shortcut.

MechKG is the strongest graph model once popularity is matched. The site-feature GBM is stronger still, which is why MechKG keeps a separate sequence channel.

Paper
Who it is for

Start from the question you have

What drives this score

Net contribution of each channel to the logit, including its background term. The intercept b₀ is shared by all pairs. Values are the mean of the five models.

Top evidence shared across models
Open the Evidence tab for the full ledger.

How much to trust it

Signals that help judge this prediction before planning an experiment.

Rules behind these signals are listed under Method.