This model is a fine-tuned model of [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://arxiv.org/abs/2007.15779) ([hugging-face card](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). The current model was developed for the web-based [ANDDigest](https://anddigest.sysbio.ru/) 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 <andsystem-candidate> tag.<br>
<br>
<b>Input:</b><br>
Any biomedical text where a name of classified object is replaced with <andsystem-candidate> tag, for example, [this](https://pubmed.ncbi.nlm.nih.gov/35128847/) pubmed abstract:<br>
<i>Merkel cell carcinoma in lymph nodes with and without primary origin. The prognosis of <b>\<andsystem-candidate\></b> 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.</i>
<br>
<br>In this example <i>MCC</i> abbreviation, which refers to the Merkel cell carcinoma, was replaced with <i>\<andsystem-candidate\></i>. Please keep in mind that maximum length of input sequence for BERT is limited to 512 tokens.
<br>
<b>Output:</b><br>
<i>LABEL_0</i> refers to the probability of the <i>FALSE</i> recognition, i.e. if the context of \<andsystem\-candidate\> doesn't corresponds to the context specific for cell components.<br>
<i>LABEL_1</i> refers to the probability of the <i>TRUE</i> recognition, i.e. when the context of \<andsystem\-candidate\> corresponds to the context specific for cell components.<br>
<br>

The optimal threshold value for the short names of cell components for the LABEL_1, was calculated using a gold standard (add link). It is<b> >= 0.9999737739562988</b>.<br>
<br>
The Mathew Correlation Coefficient of the model for the long names (\>= 15 symbols) is 0.989.<br>
The ROC AUC value of the model, calculated for the short names (\<\= 4 symbols) is 0.907.
---

{"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"}]}
transformers pytorch bert text-classification ANDDigest ANDSystem arxiv:2007.15779 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #ANDDigest #ANDSystem #arxiv-2007.15779 #autotrain_compatible #endpoints_compatible #region-us 

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 <andsystem-candidate> tag.<br>
<br>
<b>Input:</b><br>
Any biomedical text where a name of classified object is replaced with <andsystem-candidate> tag, for example, this pubmed abstract:<br>
<i>Merkel cell carcinoma in lymph nodes with and without primary origin. The prognosis of <b>\<andsystem-candidate\></b> 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.</i>
<br>
<br>In this example <i>MCC</i> abbreviation, which refers to the Merkel cell carcinoma, was replaced with <i>\<andsystem-candidate\></i>. Please keep in mind that maximum length of input sequence for BERT is limited to 512 tokens.
<br>
<b>Output:</b><br>
<i>LABEL_0</i> refers to the probability of the <i>FALSE</i> recognition, i.e. if the context of \<andsystem\-candidate\> doesn't corresponds to the context specific for cell components.<br>
<i>LABEL_1</i> refers to the probability of the <i>TRUE</i> recognition, i.e. when the context of \<andsystem\-candidate\> corresponds to the context specific for cell components.<br>
<br>

The optimal threshold value for the short names of cell components for the LABEL_1, was calculated using a gold standard (add link). It is<b> >= 0.9999737739562988</b>.<br>
<br>
The Mathew Correlation Coefficient of the model for the long names (\>= 15 symbols) is 0.989.<br>
The ROC AUC value of the model, calculated for the short names (\<\= 4 symbols) is 0.907.
---

TAGS
#transformers #pytorch #bert #text-classification #ANDDigest #ANDSystem #arxiv-2007.15779 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #ANDDigest #ANDSystem #arxiv-2007.15779 #autotrain_compatible #endpoints_compatible #region-us 
