Description
This model detects molecular biology-related terms in medical texts. The model is trained with the BertForTokenClassification method from the transformers library and imported into Spark NLP.
Predicted Entities
cell_line, cell_type, protein, DNA, RNA
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")\
sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
ner_model = MedicalBertForTokenClassifier.pretrained("bert_token_classifier_ner_jnlpba_cellular", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("ner")\
.setCaseSensitive(True)\
.setMaxSentenceLength(512)
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
ner_model,
ner_converter
])
data = spark.createDataFrame([["""The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells."""]]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val ner_model = MedicalBertForTokenClassifier.pretrained("bert_token_classifier_ner_jnlpba_cellular", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
.setCaseSensitive(True)
.setMaxSentenceLength(512)
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(document_assembler,
sentence_detector,
tokenizer,
ner_model,
ner_converter))
val data = Seq("""The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.token_bert.jnlpba_cellular").predict("""The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.""")
Results
+-------------------------------------+---------+
|ner_chunk |label |
+-------------------------------------+---------+
|protein kinase C |protein |
|IL-2 |protein |
|IFN-gamma and GM-CSF cytoplasmic mRNA|RNA |
|cytokine gene |DNA |
|IL-2 |protein |
|IL-12 |protein |
|IFN-gammaand GM-CSFgene |protein |
|NK3.3 cell line |cell_line|
|T-cell receptor |protein |
|p95vav |protein |
|T-cell antigen receptor |protein |
|TCR |protein |
|tyrosine kinases |protein |
|p95vav |protein |
|hematopoietic and trophoblast cells |cell_type|
+-------------------------------------+---------+
Model Information
| Model Name: | bert_token_classifier_ner_jnlpba_cellular |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 404.2 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
https://github.com/cambridgeltl/MTL-Bioinformatics-2016
Benchmarking
label precision recall f1-score support
B-cell_line 0.5850 0.6880 0.6324 500
I-cell_line 0.6374 0.7644 0.6952 989
B-DNA 0.7187 0.7453 0.7318 1056
I-DNA 0.8134 0.8603 0.8362 1789
B-protein 0.7286 0.8429 0.7816 5067
I-protein 0.8020 0.8129 0.8074 4774
B-RNA 0.6812 0.7966 0.7344 118
I-RNA 0.8358 0.8984 0.8660 187
B-cell_type 0.7768 0.7501 0.7632 1921
I-cell_type 0.8654 0.7887 0.8253 2991
micro-avg 0.7673 0.8065 0.7864 19392
macro-avg 0.7444 0.7948 0.7673 19392
weighted-avg 0.7722 0.8065 0.7875 19392