Sissevaade Genebench-Prosse

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:

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{
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  "answer": {
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    "therapy_class_code": <int>,
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    "benefit_rd_pp": <float>,
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    "toxicity_dropout_risk_pp": <float>,
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    "net_clinical_utility_pp": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

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.

Estimate the requested quantities.

Definitions:

• 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.

Conventions:

• all growth effects are log2(day10/day0) competitive-growth effects relative to matched controls;
• more negative numbers indicate stronger loss of fitness;
• set advance_target to 1 only if lncrna_specific_lfc <= -0.08 and neighbor_mediated_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:

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{
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  "answer": {
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    "advance_target": <int>,
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    "lncrna_specific_lfc": <float>,
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    "neighbor_mediated_lfc": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

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 SD increase in log10 concentration, conditional on the other protein.

Interpretation: theta_PROTA and theta_PROTB use the same log-odds per-SD scale defined in the goal.

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:

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{
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  "answer": {
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    "theta_PROTA": <float>,
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    "theta_PROTB": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

Using cohort_roster.tsv.gz, partner_roster.tsv.gz, calibration_controls.tsv.gz, target_metadata.tsv.gz, and assay_observations.tsv.gz, estimate residual reproductive risk for an autosomal recessive DRX1 condition. Report all quantities on the probability scale, not as percentages: carrier_frequency_afr and carrier_frequency_eur among screening-roster adults; residual_carrier_risk_afr_negative for an AFR screening-roster adult with a negative DRX1 screen; partner_carrier_frequency_full_roster for a uniformly sampled partner_roster.tsv.gz row; and couple_reproductive_risk for an affected conceptus when the index person is AFR and screen-negative and the partner is drawn from partner_roster.tsv.gz. Assume autosomal recessive inheritance with a 1/4 affected-conceptus risk conditional on both biological parents being carriers. 

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:

1
{
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  "answer": {
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    "carrier_frequency_afr": <float>,
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    "carrier_frequency_eur": <float>,
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    "residual_carrier_risk_afr_negative": <float>,
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    "partner_carrier_frequency_full_roster": <float>,
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    "couple_reproductive_risk": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

Estimate the per-allele log rate ratio for CXCL10 expression in the activated monocyte subpopulation from the provided single-cell RNA-seq data. 

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:

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{
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  "answer": {
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    "beta_activated": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

Analyze the released files for anonymous Locus Q. Estimate the full-cohort source-population clinical association and molecular expression support for the calibrated nested segment-B structural copy dosage, separating the nested segment-B dosage from the broader outer-orientation dosage. Report subhap_log_or as the natural-log source-population total-effect odds ratio for case status per additional calibrated segment-B copy. Report expression_log_fc as the natural-log expression fold-change per calibrated segment-B copy for the expression-supported gene. Report target_support_code as 1 if the supported gene has a positive expression_log_fc and the clinical association is protective (subhap_log_or < 0), otherwise 0. Report n_calibrated_carriers as the number of reliable breakpoint-panel samples carrying at least one segment-B copy. 

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:

1
{
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  "answer": {
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    "n_calibrated_carriers": <int>,
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    "target_support_code": <int>,
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    "expression_log_fc": <float>,
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    "subhap_log_or": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

You are given Hi-C contact matrices at 20 kb and 40 kb resolution plus bin annotations. Estimate the loop enrichment at the 20 kb interaction between `bin_id = 8` and `bin_id = 17` in `bins_20kb.tsv.gz`. Report three quantities: `case_loop_strength` (mean log2(observed/expected) across case replicates), `control_loop_strength` (mean log2(observed/expected) across control replicates), and `delta_loop_strength` (case minus control).

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:

1
{
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  "answer": {
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    "case_loop_strength": <float>,
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    "control_loop_strength": <float>,
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    "delta_loop_strength": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}

Map the chromosome 1 QTL in an 8-founder multi-parent population. Report the position (cM) and which founder carries the high-effect allele.

Report high_founder as "F1".."F8".

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:

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{
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"answer": {
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"high_founder": "<string>",
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"qtl_pos_cM": <float>
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},
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"reasoning": "<description of method and QC>"
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}

Data files:

• markers.tsv.gz: marker metadata
• founders.tsv.gz: founder alleles at each marker
• ril_genotypes.npz: observed RIL genotypes (biallelic)
• phenotypes.tsv.gz: phenotype and covariates

You are given phased local-ancestry tracts for one admixed individual. Estimate, for each transmitted parental haplotype, the fraction of ancestry A across the called tract span and the number of generations since a single recent admixture pulse. Label parent1 as the haplotype with the smaller ancestry-A fraction and parent2 as the haplotype with the larger ancestry-A fraction. 

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:

1
{
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  "answer": {
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    "parent1_A_fraction": <float>,
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    "parent1_t": <float>,
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    "parent2_A_fraction": <float>,
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    "parent2_t": <float>
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  },
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  "reasoning": "<description of method and QC>"
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}