Description
This model maps gene symbols to their associated HPO code(s), based on gene-phenotype associations curated by the Human Phenotype Ontology (HPO) project (reverse direction of hpo_code_gene_mapper). Genes linked to more than one HPO code return every associated code via the all_k_resolutions metadata field. Trained on the Human Phenotype Ontology (HPO) 2026-06-23 release.
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
chunk_assembler = Doc2Chunk()\
.setInputCols(["document"])\
.setOutputCol("ner_chunk")
gene_hpo_code_mapper = ChunkMapperModel.pretrained("gene_hpo_code_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["hpo_code"])
pipeline = Pipeline(stages=[document_assembler, chunk_assembler, gene_hpo_code_mapper])
data = spark.createDataFrame([["CHN1"], ["MDH1"], ["SNAP25"]]).toDF("text")
result = pipeline.fit(data).transform(data)
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
chunk_assembler = nlp.Doc2Chunk()\
.setInputCols(["document"])\
.setOutputCol("ner_chunk")
gene_hpo_code_mapper = medical.ChunkMapperModel.pretrained("gene_hpo_code_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["hpo_code"])
pipeline = nlp.Pipeline(stages=[document_assembler, chunk_assembler, gene_hpo_code_mapper])
data = spark.createDataFrame([["CHN1"], ["MDH1"], ["SNAP25"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val chunkAssembler = new Doc2Chunk()
.setInputCols("document")
.setOutputCol("ner_chunk")
val geneHpoCodeMapper = ChunkMapperModel
.pretrained("gene_hpo_code_mapper", "en", "clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("mappings")
.setRels(Array("hpo_code"))
val pipeline = new Pipeline().setStages(Array(documentAssembler, chunkAssembler, geneHpoCodeMapper))
val data = Seq("CHN1", "MDH1", "SNAP25").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| gene | hpo_code | all_k_resolutions |
|:-------|:-----------|:--------------------------------------------------------------------------------------------------------|
| CHN1 | HP:0001177 | HP:0001177:::HP:0001156:::HP:0025186:::HP:0001199:::HP:0009921:::HP:0001250:::HP:0001263:::HP:000740... |
| MDH1 | HP:0500149 | HP:0500149:::HP:0001276:::HP:0001250:::HP:0001263:::HP:0100876:::HP:0002521:::HP:0001338:::HP:000000... |
| SNAP25 | HP:0002465 | HP:0002465:::HP:0002421:::HP:0003701:::HP:0001270:::HP:0001288:::HP:0001283:::HP:0001284:::HP:000125... |
Model Information
| Model Name: | gene_hpo_code_mapper |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 771.7 KB |