Contested result

AMELIE and PhenIX: 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 PhenIX in 11 of 14 comparisons; other studies rank PhenIX above AMELIE in 12 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 PhenIX above AMELIE in 12 of its 14 comparisons and AMELIE higher in 2. Evaluation of phenotype-driven gene… ranks AMELIE above PhenIX in 11 of its 14 comparisons and PhenIX higher in 3.

Rank PhenIX 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.

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

Papers compared (2)
Paper	Role	Ranks PhenIX 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	12	2	0

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

Protocol	Dataset	Metric	PhenIX	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	15.7	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	41.6	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	54.8	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	61.3	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	69.5	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	33.8	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	74.1	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	42.6	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	73.2	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	79.9	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	PhenIX
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	84.2	81.8	PhenIX
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	64.6	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	87.1	82.8	PhenIX

Comparisons in Refined preferences of prioritizers… (14)

Protocol	Dataset	Metric	PhenIX	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	49.5	50.5	AMELIE
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	PhenIX
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	94.8	91.8	PhenIX
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	96.1	92.8	PhenIX
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	96.4	93.4	PhenIX
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	80	83.3	AMELIE
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	PhenIX
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	69.1	55.9	PhenIX
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	90.1	87.5	PhenIX
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	PhenIX
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	PhenIX
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	PhenIX
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	86.2	81.6	PhenIX
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	92.1	90.8	PhenIX

What disagrees

Other studies: rank PhenIX above AMELIE in 12 of 14 comparisons
This study: ranks AMELIE above PhenIX in 11 of 14 comparisons

Compared: AMELIE, PhenIX
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)	PhenIX 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)	15.7
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	41.6
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	54.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	61.3
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	69.5
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	33.8
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	74.1
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 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)	PhenIX 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)	42.6
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	73.2
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	79.9
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	PhenIX 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)	84.2
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	64.6
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases; Yuan et al. 2022, SM Table 3 (accuracy in each top level experiment)	PhenIX 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)	87.1

In the other studies (46 results)

In the other studies
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	PhenIX 1.16, 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)	94.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	96.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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-40 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	96.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	49.5
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	91.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	94.8
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	96.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	96.4
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	80
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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	PhenIX 1.16, Protocol A (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)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	92.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	90.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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-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	PhenIX 1.16, 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	PhenIX 1.16, 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	PhenIX 1.16, 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)	86.2
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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-50 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	PhenIX 1.16, 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)	69.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	90.1
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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	PhenIX 1.16, 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	PhenIX 1.16, 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
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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)	86.2
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases	PhenIX 1.16, 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-50 accuracy (proportion of solved cases with the causal gene within the tool's top-ranked genes)	92.1

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