Edit on GitHub
  • John Snow Labs
  • Jul 28, 2025

HPO Code To Gene Mapping

  • licensed
  • en
  • gene
  • hpo
  • mapping

Description

This pretrained model maps HPO codes to related genes. It also returns all the possible genes in the all_k_resolutions in the metadata.

Copy S3 URI

How to use


document_assembler = DocumentAssembler()\
      .setInputCol("text")\
      .setOutputCol("document")

chunk_assembler = Doc2Chunk()\
      .setInputCols(["document"])\
      .setOutputCol("hpo_code")

mapperModel = ChunkMapperModel.pretrained("hpo_code_gene_mapper", "en", "clinical/models")\
    .setInputCols(["hpo_code"])\
    .setOutputCol("mappings")\
    .setRels(["gene"])

mapper_pipeline = Pipeline(stages=[
    document_assembler,
    chunk_assembler,
    mapperModel
])

data = spark.createDataFrame([["HP:0000002"],["HP:6001080"],["HP:0009484"]]).toDF("text")

result = mapper_pipeline.fit(data).transform(data)



document_assembler = nlp.DocumentAssembler()\
      .setInputCol("text")\
      .setOutputCol("document")

chunk_assembler = nlp.Doc2Chunk()\
      .setInputCols(["document"])\
      .setOutputCol("hpo_code")

mapperModel = medical.ChunkMapperModel.pretrained("hpo_code_gene_mapper", "en", "clinical/models")\
    .setInputCols(["hpo_code"])\
    .setOutputCol("mappings")\
    .setRels(["gene"])

mapper_pipeline = nlp.Pipeline(stages=[
    document_assembler,
    chunk_assembler,
    mapperModel
])

data = spark.createDataFrame([["HP:0000002"],["HP:6001080"],["HP:0009484"]]).toDF("text")

result = mapper_pipeline.fit(data).transform(data)



val document_assembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")

val chunk_assembler = new Doc2Chunk()
      .setInputCols("document")
      .setOutputCol("hpo_code")

val mapperModel = ChunkMapperModel.pretrained("hpo_code_gene_mapper", "en", "clinical/models")
    .setInputCols("hpo_code")
    .setOutputCol("mappings")
    .setRels(Array("gene"))

val mapper_pipeline = new Pipeline().setStages(Array(
    document_assembler,
    chunk_assembler,
    mapperModel
))


val data = Seq("HP:0000002","HP:6001080","HP:0009484").toDF("text")

val result = mapper_pipeline.fit(data).transform(data)

Results


+----------+-------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|  hpo_code|   gene|                                                                                                                                                         all_k_resolutions|
+----------+-------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|HP:0000002|  DUSP6|DUSP6:::FGF8:::FGFR1:::GNRH1:::GNRHR:::KISS1:::LRP5:::NHLH2:::TAC3:::TACR3:::FGF17:::NOG:::COPB2:::HS6ST1:::NSMF:::CHD7:::WDR11:::PROK2:::SPRY4:::KISS1R:::TMEM67:::PROKR2|
|HP:6001080|HSD11B1|                                                                                                                                                                HSD11B1:::|
|HP:0009484|    SHH|                                                                                                                                                               SHH:::LMBR1|
+----------+-------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Model Information

Model Name: hpo_code_gene_mapper
Compatibility: Healthcare NLP 6.0.4+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 920.9 KB