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589 lines
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21 KiB
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Text Mining Tools - NIH
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<main class="usa-width-one-whole journal-container">
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<h3>Web applications</h3>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.ncbi.nlm.nih.gov/research/pubtator3/'>
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PubTator
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_1'>
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<b>Description</b><br>
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PubTator is a text-mining tool for annotating the entire PubMed articles with key biological entities (e.g. genes & diseases) and is available through both Web and API access.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar/'>
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LitVar
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_2'>
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<b>Description</b><br>
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LitVar is a semantic search engine for linking genomic variant data in PubMed and PMC
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.ncbi.nlm.nih.gov/research/coronavirus/'>
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LitCovid
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_14'>
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<b>Description</b><br>
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LitCovid is a literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus, first created in February 2020.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.ncbi.nlm.nih.gov/research/litsuggest/'>
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LitSuggest
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_15'>
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<b>Description</b><br>
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LitSuggest is a web-based system for literature triage and document classification using AI and machine learning.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.ncbi.nlm.nih.gov/research/litsense/'>
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LitSense
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_16'>
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<b>Description</b><br>
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LitSense helps make sense of the biomedical literature at the sentence level by finding the best-matching sentences given a query via a cutting-edge neural embedding approach.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://www.teamtat.org'>
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TeamTat
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_17'>
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<b>Description</b><br>
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TeamTat is a web-based text annotation tool for biomedical text and beyond.
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</div>
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</div>
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</div>
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</div>
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<div>
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<h3>Software</h3>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/taggerone'>
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TaggerOne (All-purpose tagger)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_5'>
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<b>Description</b><br>
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TaggerOne is a general toolkit for biomedical named entity recognition and normalization. As a machine learning system it is not entity-specific but does require training data.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/tmchem'>
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tmChem (chemical tagger)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_6'>
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<b>Description</b><br>
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tmChem is an open-source software tool for identifying chemical names. tmChem achieved the highest performance in BioCreative CHEMDNER task (over 87% F-measure)
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/dnorm'>
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DNorm (disease tagger)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_7'>
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<b>Description</b><br>
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DNorm is the first technique to use machine learning to recognize and normalize disease names in biomedical text.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/gnormplus'>
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GNormPlus (gene tagger)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_8'>
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<b>Description</b><br>
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GNormPlus is an end-to-end system that handles both gene/protein name and identifier detection in biomedical literature, including gene/protein mentions, family names and domain names.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/sr4gn'>
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SR4GN (species tagger)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_9'>
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<b>Description</b><br>
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SR4GN is an open source tool for species recognition/disambiguation and is optimized for the Gene Normalization task.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/tmvar'>
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tmVar (mutation tagger)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_10'>
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<b>Description</b><br>
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tmVar extracts a wide range of sequence variants in both protein and gene levels (e.g. substitution, deletion, etc) in HGVS formats.
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='/bionlp/Tools/simconcept'>
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SimConcept (text simplification)
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_11'>
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<b>Description</b><br>
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SimConcept uses patterns to identify individual mentions from a composite named entity (e.g. SMAD 1, 5, and 8).
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</div>
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</div>
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</div>
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<div class="issue labs-docsums labs-content-box ">
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<div class='tool'>
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<div class='usa-width-one-fourth'>
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<div class='toolname'>
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<a href='https://github.com/ncbi-nlp/NegBio'>
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NegBio
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</a>
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</div>
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</div>
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<div class='usa-width-three-fourths tooldesc' id='tool_13'>
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<b>Description</b><br>
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NegBio is a high-performance tool for negation and uncertainty detection in clinical text (e.g. radiology reports).
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</div>
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</div>
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</div>
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</div>
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</main>
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</main>
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</div>
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</div>
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</div>
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<footer class="usa-footer usa-footer-big ncbi-footer" role="contentinfo">
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<div class="usa-grid">
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<div class="usa-row">
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<div class="usa-width-one-half">
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<div>
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<div class="org-section">
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<a href="https://www.hhs.gov/"><img class="usa-footer-logo-img hhs-logo"
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src="/research/bionlp/static/base/images/dhhs-logo-white.svg"
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alt="U.S. Department of Health & Human Services">
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<span class="usa-sr-only">Department of Health and Human Services</span></a>
|
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<a href="https://www.nih.gov/"><img class="usa-footer-logo-img nih-logo"
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src="/research/bionlp/static/base/images/nih-logo-white.svg"
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alt="National Institutes of Health">
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<span class="usa-sr-only">National Institutes of Health</span></a>
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<a href="https://www.nlm.nih.gov/"><img class="usa-footer-logo-img nlm-logo"
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src="/research/bionlp/static/base/images/nlm-logo-letters-white.svg"
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alt="National Library of Medicine">
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<span class="usa-sr-only">National Library of Medicine</span></a>
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<a href="https://www.usa.gov/"><img class="usa-footer-logo-img usagov-logo"
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src="/research/bionlp/static/base/images/usagov-logo-white.svg"
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alt="USA.gov"/>
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<span class="usa-sr-only">USA.gov</span></a>
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</div>
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</div>
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</div>
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