Description
This model maps gene symbols and official 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_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_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_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:1101:::HGNC:28470:::HGNC:58363:::HGNC:25008:::HGNC:1099:::HGNC:... | 0.0000:::0.0690:::0.0980:::0.1656:::0.1882:::0.1962:::0.2005:::0.2085:::0.2125::... | BRCA1 [BRCA1 DNA repair associated]:::BRCA2 [BRCA2 DNA repair associated]:::BRCA... | protein-coding gene :: gene with protein product:::protein-coding gene :: gene w... |
| EGFR | GENE | HGNC:3236 | EGFR [epidermal growth factor receptor] | HGNC:3236:::HGNC:3229:::HGNC:40207:::HGNC:26810:::HGNC:3235:::HGNC:9600:::HGNC:2... | 0.0000:::0.1780:::0.2620:::0.2658:::0.2740:::0.2741:::0.2873:::0.2949:::0.3080::... | EGFR [epidermal growth factor receptor]:::EGF [epidermal growth factor]:::EGFR-A... | 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:7108:::HGNC:4021:::HGNC:6215:::HGNC:5176:::HGNC:303... | 0.0000:::0.2699:::0.3207:::0.3300:::0.3371:::0.3408:::0.3481:::0.3614:::0.3659::... | KRAS [KRas proto-oncogene, GTPase]:::KRASP1 [KRas proto-oncogene, GTPase pseudog... | protein-coding gene :: gene with protein product:::pseudogene :: pseudogene:::pr... |
Model Information
| Model Name: | biolordresolve_hgnc_2026 |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings] |
| Output Labels: | [resolution] |
| Language: | en |
| Size: | 260.8 MB |
| Case sensitive: | false |