From 094fbe9f9ab8ef30aaa5bbacdac3720364d9cb1f Mon Sep 17 00:00:00 2001
From: Harry Caufield <j.harry.caufield@gmail.com>
Date: Mon, 2 Aug 2021 14:12:17 -0700
Subject: [PATCH] Copyediting

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 1 file changed, 5 insertions(+), 5 deletions(-)

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@@ -114,16 +114,16 @@ <h2><a target="_blank" href="http://caseolap.github.io">CaseOLAP</a></h2>
             <div class="page-header"><h1>CaseOLAP Platform </h1></div>
             <div class="col-md-7">
                 <p>
-                    We are greatly excited with the new enthusiasm to introduce the Context-aware Semantic Online Analytical Processing pipeline (CaseOLAP), developed in 2016. The rapidly accumulating quantity of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports.CaseOLAP successfully quantifies user-defined phrase-category relationships through analysis of textual data.
+                    We are excited to introduce the Context-aware Semantic Online Analytical Processing pipeline (CaseOLAP), originally developed in 2016 through the efforts of computer science researchers (the KnowEng group at University of Illinois at Urbana-Champaign) and biomedical informatics experts (the HeartBD2K group at UCLA). The rapidly accumulating quantity of biomedical text data has far exceeded the human capacity for manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. CaseOLAP successfully quantifies user-defined phrase-category relationships through analysis of text data.
                 </p>
                  <p>
-                    We have developed a protocol for the complete CaseOLAP platform, including data preprocessing (i.e., downloading and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube and quantifying phrase-category relationships using the core CaseOLAP algorithm. 
+                    We have developed an open protocol for the complete CaseOLAP platform, including data preprocessing (i.e., downloading and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called a Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. 
                 </p>
                 <p>
-                    Data preprocessing generates key-value pairs for all documents involved. As an example, a key may refer to the document PMID, while a value may refer to different document metadata. Preprocessed data is rearranged by indexing and searching for an entity count, which further facilitates the CaseOLAP score calculation. Obtained raw CaseOLAP results can be taken to integrative analysis including dimensionality reduction, clustering, temporal and geographical analysis, as well as the creation of a graphical database which enables semantic mapping of the documents . 
+                    Data preprocessing generates key-value pairs for all documents in the input corpus. As an example, a key may refer to the document PubMed ID, while a value may refer to different document metadata. Preprocessed data is rearranged by indexing and searching for an entity count, which further facilitates the CaseOLAP score calculation. Obtained raw CaseOLAP results are ideal for integrative analysis including dimensionality reduction, clustering, or temporal and geographical analysis. The output may also be loaded into a graph database, enabling semantic mapping of the documents. 
                 </p>
                 <p>
-                    CaseOLAP defines phrase-category relationships in an accurate (pinpoints relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following our protocol, one can build up a cloud-computing environment supporting CaseOLAP which offers enhanced accessibility and affords grand opportunities to empower the biomedical community with phrase-mining tools for widespread research applications. 
+                    CaseOLAP defines phrase-category relationships in an accurate (pinpoints relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following our protocol, one can build a cloud-computing environment to offers enhanced accessibility and support grand opportunities to empower the biomedical community with phrase-mining tools for widespread research applications. 
                  <p>
             </div>
             <div class="col-md-4 col-md-offset-1">
@@ -135,7 +135,7 @@ <h2> Applications </h2>
                     <li> <a href="association/index.html" target="_blank"> Discovering Protein-Disease Attribution </a> </li>
                     <li> <a href="https://caseolap.github.io/dataviz/" target="_blank"> Sample CaseOLAP score visualization </a> </li>
                     <li> <a href="graph/index.html" target="_blank"> Sample of co-occurance based graph </a> </li>
-                    <li> <a href="https://caseolap.github.io/reactom-web-api/" target="_blank"> Reactom API </a> </li>
+                    <li> <a href="https://caseolap.github.io/reactom-web-api/" target="_blank"> Reactome API </a> </li>
                     
                     <li> Discovering Drug-Disease Association </li>
                     <li> Temporal Analysis of Scientific Innovation </li>