Inside Genebench-Pro

PUBLISHER ARTICLE PREVIEW

From the original article

These 10 case studies showcase representative questions from GeneBench-Pro. Each case study includes the original prompt, datasets, and supporting materials. For an overview of the benchmark and key findings, see the announcement blog.

Estimate whether a synthetic TXR1-directed inhibitor has positive clinical utility in tumors whose target activation is driven by a structural variant. TXR1, TXR1i, DLR1, and star-allele labels are synthetic benchmark labels.

The target subgroup has to be recovered from long-read, expression, tumor-quality, and pharmacogenomic evidence before benefit and toxicity can be interpreted as a treatment decision.

A molecular tumor board registry contains trial-eligible advanced solid-tumor cases considered for a TXR1-directed inhibitor. Estimate, for tumors with SV-driven TXR1 target-mediated activation at time zero, the marginal effect of TXR1i versus non-TXR1 systemic therapy on week-16 clinical benefit as if all patients had an assessable week-16 visit. Also estimate the 8-week treatment-limiting toxicity/discontinuation risk under TXR1i in the same target population.

Report net clinical utility = benefit risk difference (percentage points) - 0.35 * toxicity risk (percentage points), and choose therapy_class_code 1 if TXR1i has positive net utility and 0 otherwise.

Use percentage-point units for all non-code quantities. Positive benefit means TXR1i improves week-16 clinical benefit relative to non-TXR1 systemic therapy.

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.Do not wrap the JSON in markdown.Do not add prose before or after the JSON.Do not omit any keys shown in the example.Return the JSON object in your final answer:

Decide whether an apparent lncRNA dependency is transcript-specific or driven by nearby-locus and neighbor-gene effects.

Transcript-directed evidence has to survive controls for local DNA-locus perturbation, neighbor-gene repression, guide swaps, GC toxicity, and plate effects.

You are given pooled CRISPRi screening data, guide-level local expression measurements, transcript-targeting CasRx follow-up data, and single-guide follow-up growth measurements for a nominated lncRNA program (LINC473) and a nearby coding gene (KIN1). The identifiers LINC473, KIN1, and ANKRD42 are synthetic benchmark labels; any resemblance to real human genes is coincidental.

lncrna_specific_lfc: the pooled-screen matched-control day-10 log2 growth effect expected at 100% effective knockdown of the dominant LINC473 transcript, not local DNA-locus effects.

neighbor_mediated_lfc: the pooled-screen matched-control day-10 log2 growth effect expected at 100% KIN1 repression in the local LINC473-locus model after accounting for concomitant LINC473 transcript knockdown.

advance_target: 1 if the evidence supports advancing LINC473 as a transcript-directed target, else 0.

all growth effects are log2(day10/day0) competitive-growth effects relative to matched controls;

set advance_target to 1 only if lncrna_specific_lfc -0.25; otherwise 0.

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.Do not wrap the JSON in markdown.Do not add prose before or after the JSON.Do not omit any keys shown in the example.Return the JSON object in your final answer:

Estimate direct disease effects for two nearby proteins using cis multivariable Mendelian randomization (cis-MVMR) while handling assay scale, allele orientation, winner's curse, LD, and residual local pleiotropy.

The two proteins share a correlated locus. The analysis has to move from marginal associations to conditional, LD-aware disease effects on a common protein scale.

You are given association summary statistics and metadata for two nearby proteins (PROTA and PROTB), a binary disease outcome, a locus correlation reference, and protein measurement records.

Goal: estimate the direct log-odds effect of each protein on the disease outcome per +1