Description
This model maps gene symbols, official names, and their known alias/previous symbols and names to HUGO Gene Nomenclature Committee (HGNC) identifiers using mpnet_embeddings_biolord_2023_c embeddings. Trained on the HGNC monthly release dated 2026-08-04.
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = 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_model = 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"])
chunk2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
embedder = MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c", "en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)\
.setBatchSize(1)
resolver = SentenceEntityResolverModel.pretrained("biolordresolve_hgnc_augmented_2026","en","clinical/models")\
.setInputCols(["embeddings"])\
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
pipeline = Pipeline(stages=[\
documentAssembler, sentenceDetector, tokenizer, word_embeddings,\
ner_model, ner_converter, chunk2doc, embedder, resolver\
])
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)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = 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_model = 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"])
chunk2doc = nlp.Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
embedder = nlp.MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c", "en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)\
.setBatchSize(1)
resolver = medical.SentenceEntityResolverModel.pretrained("biolordresolve_hgnc_augmented_2026","en","clinical/models")\
.setInputCols(["embeddings"])\
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
pipeline = nlp.Pipeline(stages=[\
documentAssembler, sentenceDetector, tokenizer, word_embeddings,\
ner_model, ner_converter, chunk2doc, embedder, resolver\
])
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 word_embeddings = WordEmbeddingsModel
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("word_embeddings")
val ner_model = MedicalNerModel
.pretrained("ner_human_phenotype_gene_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("GENE"))
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_chunk")
.setOutputCol("ner_chunk_doc")
val embedder = MPNetEmbeddings
.pretrained("mpnet_embeddings_biolord_2023_c", "en")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("embeddings")
.setCaseSensitive(false)
.setBatchSize(1)
val resolver = SentenceEntityResolverModel
.pretrained("biolordresolve_hgnc_augmented_2026", "en", "clinical/models")
.setInputCols(Array("embeddings"))
.setOutputCol("resolution")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(
documentAssembler, sentenceDetector, tokenizer, word_embeddings,
ner_model, ner_converter, chunk2doc, embedder, resolver
))
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 res = pipeline.fit(data).transform(data)
Results
| ner_chunk | entity | HGNC Code | Resolution | all_k_results | all_k_cosine_distances | all_k_resolutions | all_k_aux_labels |
|:------------|:---------|:------------|:----------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| BRCA1 | GENE | HGNC:1100 | BRCA1 [BRCA1 DNA repair associated] | HGNC:1100:::HGNC:15550:::HGNC:1101:::HGNC:28470:::HGNC:24324:::HGNC:58363:::HGNC... | 0.0000:::0.0599:::0.0690:::0.0980:::0.1376:::0.1656:::0.1767:::0.1831:::0.1859::... | BRCA1 [BRCA1 DNA repair associated]:::BRCAA1 [AT-rich interaction domain 4B]:::B... | protein-coding gene :: gene with protein product:::protein-coding gene :: gene w... |
| EGFR | GENE | HGNC:3236 | EGFR [epidermal growth factor receptor] | HGNC:3236:::HGNC:20561:::HGNC:3229:::HGNC:13780:::HGNC:54482:::HGNC:3665:::HGNC:... | 0.0000:::0.0956:::0.1780:::0.1925:::0.2010:::0.2355:::0.2438:::0.2489:::0.2594::... | EGFR [epidermal growth factor receptor]:::EGFR-RS [rhomboid 5 homolog 1]:::EGF [... | protein-coding gene :: gene with protein product:::protein-coding gene :: gene w... |
| KRAS | GENE | HGNC:6407 | KRAS [KRas proto-oncogene, GTPase] | HGNC:6407:::HGNC:6406:::HGNC:17898:::HGNC:17899:::HGNC:4221:::HGNC:28932:::HGNC:... | 0.0000:::0.1855:::0.2429:::0.2629:::0.2645:::0.2857:::0.2925:::0.3058:::0.3062::... | KRAS [KRas proto-oncogene, GTPase]:::KRAS1P [KRas proto-oncogene, GTPase pseudog... | protein-coding gene :: gene with protein product:::pseudogene :: pseudogene:::pr... |
Model Information
| Model Name: | biolordresolve_hgnc_augmented_2026 |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings] |
| Output Labels: | [resolution] |
| Language: | en |
| Size: | 566.4 MB |
| Case sensitive: | false |