Contested result

AMELIE and Exomiser: Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases reverses the order

Low severity
Not yet reviewed
Ordering reversal
Different tasks across studies

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases ranks AMELIE above Exomiser in 11 of 14 comparisons; other studies rank Exomiser above AMELIE in 14 of 14.

An automatic lead, not a finding of error. The results below are shown as published, everywhere on the site, until a reviewer decides otherwise.

Which studies agree

Refined preferences of prioritizers… ranks Exomiser above AMELIE in all 14 of its comparisons. Evaluation of phenotype-driven gene… ranks AMELIE above Exomiser in 11 of its 14 comparisons and Exomiser higher in 3.

Rank Exomiser higher (1): Refined preferences of prioritizers…
Rank AMELIE higher (1): Evaluation of phenotype-driven gene… (disagrees)
Mixed (0): none

Each paper sits where at least two thirds of its comparisons point; Mixed means neither model reaches two thirds. The larger, outlined dot is the paper that disagrees.

Exomiser higherTieAMELIE higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (2)
Paper	Role	Ranks Exomiser higher	Ranks AMELIE higher	Ties
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases and 1 more record · DOI	Disagrees	3	11	0
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases · DOI	Agrees	14	0	0

Comparisons in Evaluation of phenotype-driven gene… (14)

Protocol	Dataset	Metric	Exomiser	AMELIE	Higher
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	15.1	47.9	AMELIE
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	53.1	86.2	AMELIE
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	70.5	90.5	AMELIE
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	79	92.8	AMELIE
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	83.9	93.1	AMELIE
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	38.4	81.3	AMELIE
Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	DDD 305 solved probands (Yuan et al. selection)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	86.6	93.1	AMELIE
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	26.3	57.9	AMELIE
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	75.1	78.9	AMELIE
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	78.9	81.3	AMELIE
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	82.8	81.8	Exomiser
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	85.2	81.8	Exomiser
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	62.2	73.7	AMELIE
Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	KingMed Changsha in-house cohort, 209 solved cases (Yuan et al. 2022)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	85.6	82.8	Exomiser

Comparisons in Refined preferences of prioritizers… (14)

Protocol	Dataset	Metric	Exomiser	AMELIE	Higher
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	61.6	50.5	Exomiser
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	91.8	90.5	Exomiser
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	95.1	91.8	Exomiser
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	96.7	92.8	Exomiser
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	97.4	93.4	Exomiser
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	86.6	83.3	Exomiser
Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	DDD 305 solved probands (Yuan et al. selection)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	97.4	93.4	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	68.4	55.9	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	89.5	87.5	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	92.1	88.8	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	92.1	90.1	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	92.1	90.8	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	88.2	81.6	Exomiser
Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	KingMed in-house trio cohort, 152 families (Yuan et al. 2024)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes), percent	92.8	90.8	Exomiser

What disagrees

Other studies: rank Exomiser above AMELIE in 14 of 14 comparisons
This study: ranks AMELIE above Exomiser in 11 of 14 comparisons

Compared: AMELIE, Exomiser
Study that disagrees: Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases, Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)
Other studies: Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases
Rule: Ordering reversal; different tasks across studies
Model identity: Matched across studies partly through links not yet reviewed

The compared numbers

Each value links to its result record and source. Compare values within one protocol; the rule compares rankings, not raw values across protocols.

In the study that disagrees (28)
Study	Tool	Protocol or benchmark	Metric	Value
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	47.9
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	86.2
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	90.5
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	93.1
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	81.3
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	93.1
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	15.1
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	53.1
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	70.5
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	79
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	83.9
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	38.4
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 305 DDD exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	86.6
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	57.9
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	78.9
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	81.3
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	81.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	81.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	73.7
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	AMELIE default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	82.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	26.3
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	75.1
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	78.9
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	82.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	85.2
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	62.2
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	Exomiser default, singleton (Yuan et al. 2022)	Causal-gene rank in 209 in-house exomes, default singleton runs (Yuan et al. 2022 SM Table 3)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	85.6

In the other studies (40)
Study	Tool	Protocol or benchmark	Metric	Value
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol A (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	93.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	93.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	50.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	90.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	91.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	83.3
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	50.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	90.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	91.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	91.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	97.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	61.6
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	95.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	96.7
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	97.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	86.6
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 305 DDD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	97.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	87.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	88.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	90.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	90.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	90.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	81.6
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	55.9
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	AMELIE 3.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	81.6
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol B (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	89.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol C (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-5 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	88.2
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-1 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	68.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-10 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	89.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-20 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-30 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	Exomiser 13.1.0, Protocol D (Yuan et al. 2024)	Causal-gene rank in 152 KGD trio-family exomes across parameter protocols (Yuan et al. 2024 Table 1)	Top-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1

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