scispacy

pip install scispacy
pip install <Model URL>

import scispacy
import spacy
nlp = spacy.load("en_core_sci_sm")
text = """
Myeloid derived suppressor cells (MDSC) are immature 
myeloid cells with immunosuppressive activity. 
They accumulate in tumor-bearing mice and humans 
with different types of cancer, including hepatocellular 
carcinoma (HCC).
"""
doc = nlp(text)
print(list(doc.sents))
>>> ["Myeloid derived suppressor cells (MDSC) are immature myeloid cells with immunosuppressive activity.", 
     "They accumulate in tumor-bearing mice and humans with different types of cancer, including hepatocellular carcinoma (HCC)."]
# Examine the entities extracted by the mention detector.
# Note that they don't have types like in SpaCy, and they
# are more general (e.g including verbs) - these are any
# spans which might be an entity in UMLS, a large
# biomedical database.
print(doc.ents)
>>> (Myeloid derived suppressor cells,
     MDSC,
     immature,
     myeloid cells,
     immunosuppressive activity,
     accumulate,
     tumor-bearing mice,
     humans,
     cancer,
     hepatocellular carcinoma,
     HCC)
# We can also visualise dependency parses
# (This renders automatically inside a jupyter notebook!):
from spacy import displacy
displacy.render(next(doc.sents), style='dep', jupyter=True)
# See below for the generated SVG.
# Zoom your browser in a bit!