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  • John Snow Labs
  • Aug 07, 2026

Mapping Gene Entities with Corresponding HGNC Codes - Augmented

  • en
  • chunk_mapper
  • licensed
  • clinical
  • hgnc
  • gene
  • augmented

Description

This model maps gene mentions extracted from clinical or biomedical text to their corresponding HUGO Gene Nomenclature Committee (HGNC) identifiers, including known alias and previous gene symbols/names in addition to current approved ones. Trained on the HGNC monthly release dated 2026-08-04.

Live Demo Open in Colab Copy S3 URI

How to use

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

sentence_detector = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
    .setInputCols(["sentence","token"])\
    .setOutputCol("word_embeddings")

ner_hgnc = MedicalNerModel.pretrained("ner_human_phenotype_gene_clinical","en","clinical/models")\
    .setInputCols(["sentence","token","word_embeddings"])\
    .setOutputCol("ner")

ner_converter = NerConverterInternal()\
    .setInputCols(["sentence","token","ner"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["GENE"])

hgnc_mapper = ChunkMapperModel.pretrained("hgnc_mapper_augmented_2026","en","clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["hgnc_id"])\
    .setLowerCase(True)

pipeline = Pipeline(stages=[\
    document_assembler, sentence_detector, tokenizer, word_embeddings,\
    ner_hgnc, ner_converter, hgnc_mapper\
])

data = spark.createDataFrame([["Genetic testing confirmed a pathogenic BRCA1 variant, and the report also flagged elevated risk associated with the EGFR and KRAS genes."]]).toDF("text")
result = pipeline.fit(data).transform(data)

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

sentence_detector = nlp.SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = nlp.Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
    .setInputCols(["sentence","token"])\
    .setOutputCol("word_embeddings")

ner_hgnc = medical.NerModel.pretrained("ner_human_phenotype_gene_clinical","en","clinical/models")\
    .setInputCols(["sentence","token","word_embeddings"])\
    .setOutputCol("ner")

ner_converter = medical.NerConverterInternal()\
    .setInputCols(["sentence","token","ner"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["GENE"])

hgnc_mapper = medical.ChunkMapperModel.pretrained("hgnc_mapper_augmented_2026","en","clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["hgnc_id"])\
    .setLowerCase(True)

pipeline = nlp.Pipeline(stages=[\
    document_assembler, sentence_detector, tokenizer, word_embeddings,\
    ner_hgnc, ner_converter, hgnc_mapper\
])

data = spark.createDataFrame([["Genetic testing confirmed a pathogenic BRCA1 variant, and the report also flagged elevated risk associated with the EGFR and KRAS genes."]]).toDF("text")
result = pipeline.fit(data).transform(data)


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

val sentenceDetector = new SentenceDetector()
    .setInputCols(Array("document"))
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val wordEmbeddings = WordEmbeddingsModel
    .pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("word_embeddings")

val nerHgnc = MedicalNerModel
    .pretrained("ner_human_phenotype_gene_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "word_embeddings"))
    .setOutputCol("ner")

val nerConverter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")
    .setWhiteList(Array("GENE"))

val hgncMapper = ChunkMapperModel
    .pretrained("hgnc_mapper_augmented_2026", "en", "clinical/models")
    .setInputCols("ner_chunk")
    .setOutputCol("mappings")
    .setRels(Array("hgnc_id"))
    .setLowerCase(true)

val pipeline = new Pipeline().setStages(Array(
    documentAssembler, sentenceDetector, tokenizer, wordEmbeddings,
    nerHgnc, nerConverter, hgncMapper
))

val data = Seq("Genetic testing confirmed a pathogenic BRCA1 variant, and the report also flagged elevated risk associated with the EGFR and KRAS genes.").toDF("text")
val result = pipeline.fit(data).transform(data)

Results

| chunk   | HGNC Code   | all_k_resolutions   |
|:--------|:------------|:--------------------|
| BRCA1   | HGNC:1100   | HGNC:1100:::        |
| EGFR    | HGNC:3236   | HGNC:3236:::        |
| KRAS    | HGNC:6407   | HGNC:6407:::        |

Model Information

Model Name: hgnc_mapper_augmented_2026
Compatibility: Healthcare NLP 6.4.1+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 3.6 MB