tags:
- ANDDigest
- ANDSystem
extra_gated_fields:
I agree to share my contact information: checkbox
widget:
- text: >-
Merkel cell carcinoma in lymph nodes with and without primary origin. The
prognosis of <andsystem-candidate> with lymph node involvement was better
in patients with an unknown than a known primary. Treatment with a uniform
aggressive combined chemoradiation regimen, with or without
lymphadenectomy, led to better survival rates than previously reported.
example_title: MCC
- text: >-
Multiplication of Motor-Driven Microtubules for Nanotechnological
Applications. Microtubules gliding on motor-functionalized surfaces have
been explored for various nanotechnological applications. However, when
moving over large distances (several millimeters) and long tens of
minutes, microtubules are lost due to surface detachment. Here, we
demonstrate the multiplication of kinesin-1-driven microtubules that
comprises two concurrent processes: (i) severing of microtubules by the
enzyme spastin and (ii) elongation of microtubules by self-assembly of
tubulin dimers at the <andsystem-candidate> ends. We managed to balance
the individual processes such that the average length of the microtubules
stayed roughly constant over time while their number increased. Moreover,
we show microtubule multiplication in physical networks with topographical
channel structures. Our method is expected to broaden the toolbox for
microtubule-based in vitro applications by counteracting the microtubule
loss from substrate surfaces. Among others, this will enable upscaling of
network-based biocomputation, where it is vital to increase the number of
microtubules during operation.
example_title: microtubule
This model is a fine-tuned model of BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext (hugging-face card). The current model was developed for the web-based ANDDigest system for the classification of the short names of cell components in texts on the basis of their context (the name considered to be short if it's length is 4 symbols or less). The analyzed name should be replaced in text with tag.
Input:
Any biomedical text where a name of classified object is replaced with tag, for example, this pubmed abstract:
Merkel cell carcinoma in lymph nodes with and without primary origin. The prognosis of <andsystem-candidate> with lymph node involvement was better in patients with an unknown than a known primary. Treatment with a uniform aggressive combined chemoradiation regimen, with or without lymphadenectomy, led to better survival rates than previously reported.
In this example MCC abbreviation, which refers to the Merkel cell carcinoma, was replaced with <andsystem-candidate>. Please keep in mind that maximum length of input sequence for BERT is limited to 512 tokens.
Output:
LABEL_0 refers to the probability of the FALSE recognition, i.e. if the context of <andsystem-candidate> doesn't corresponds to the context specific for cell components.
LABEL_1 refers to the probability of the TRUE recognition, i.e. when the context of <andsystem-candidate> corresponds to the context specific for cell components.