This work was supported in part by National Heart, Lung, and Blood Institute: R35 HL135772 (to P. Ping); National Institute of General Medical Sciences: U54 GM114833 (to P. Ping, K. Watson, and W. Wang); U54 GM114838 (to J. Han); a gift from the Hellen &#38; Larry Hoag Foundation and Dr. S. Setty; and the T.C. Laubisch endowment at UCLA (to P. Ping).

NOTE: We have developed this protocol based on the Python programming language. To run this program, have Anaconda Python and Git pre-installed on the device. The commands provided in this protocol are based on Unix environment. This protocol provides the detail of downloading data from PubMed (MEDLINE) database, parsing the data, and setting up a cloud computing platform for the phrase mining and quantification of user-defined entity-category association.
1.Getting code and python environment setup
<ol>
	<li>Download or clone the code repository from Github (<a href="https://github.com/CaseOLAP/caseolap.git">https://github.com/CaseOLAP/caseolap</a>) or by typing &#8216;git clone <a href="https://github.com/CaseOLAP/caseolap.git">https://github.com/CaseOLAP/caseolap.git</a>&#8217; in the terminal window.</li>
	<li>Navigate to the &#8216;caseolap&#8217; directory. This is the root directory of the project. Within this directory, the &#8216;data&#8217; directory will be populated with multiple data sets as you progress through these steps in the protocol. The &#8216;input&#8217; directory is for user-provided data. The &#8216;log&#8217; directory has log files for troubleshooting purposes. The &#8216;result&#8217; directory is where the final results will be stored.</li>
	<li>Using the terminal window, go to the directory where you cloned our GitHub repository. Create the CaseOLAP environment using the &#8216;environment.yml&#8217; file by typing &#8216;conda env create -f environment.yaml&#8217; in the terminal. Then activate the environment by typing &#8216;source activate caseolap&#8217; in the terminal.</li>
</ol>
2. Downloading documents
<ol>
	<li>Make sure that the FTP address in &#8216;ftp_configuration.json&#8217; in the config directory is the same as the Annual Baseline or Daily Update Files link address, found in the link (<a href="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">https://www.nlm.nih.gov/databases/download/pubmed_medline.html</a>).</li>
	<li>To download baseline only or update files only, set &#8216;true&#8217; in the &#8216;download_config.json&#8217; file in the &#8216;config&#8217; directory. By default, it downloads and extracts both baseline and update files. A sample of extracted XML data can be viewed at (<a href="https://github.com/CaseOLAP/caseolap-pipelines/blob/master/data/extracted-data-sample.xml">https://github.com/CaseOLAP/caseolap-pipelines/blob/master/data/extracted-data-sample.xml</a>)</li>
	<li>Type &#8216;python run_download.py&#8217; in the terminal window to download abstracts from the Pubmed database. This will create a directory called &#8216;ftp.ncbi.nlm.nih.gov&#8217; in the current directory. This process checks the integrity of the downloaded data and extracts it to the target directory.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;download_log.txt&#8217; in case the download process fails. If the process is completed successfully, the debugging messages of the download process will be printed out in this log file.</li>
	<li>When the download is complete, navigate through &#8216;ftp.ncbi.nlm.nih.gov&#8217; to make sure that there is &#8216;updatefiles&#8217; or &#8216;basefiles&#8217; or both directories based on download configuration in &#8216;download_config.json&#8217;. The file statistics become available at &#8216;filestat.txt&#8217; in the &#8216;data&#8217; directory.</li>
</ol>
3. Parsing documents
<ol>
	<li>Make sure that downloaded and extracted data is available at &#8216;ftp.ncbi.nlm.nih.gov&#8217; directory from step 2. This directory is the input data directory in this step.</li>
	<li>To modify the data-parsing schema, select parameters in &#8216;parsing_config.json&#8217; file in the &#8216;config&#8217; directory by setting their value to &#8216;true&#8217;. By default, it parses the PMID, authors, abstract, MeSH, location, journal, publication date.</li>
	<li>Type &#8216;python run_parsing.py&#8217; in the terminal to parse the documents from downloaded (or extracted) files. This step parses all downloaded XML files and creates a python dictionary for each document with keys (e.g., PMID, authors, abstract, MeSH of the file based on parsing schema setup at step 3.2).</li>
	<li>Once data parsing is completed, make sure that parsed data is saved in the file called &#8216;pubmed.json&#8217; in the data directory. A sample of parsed data is available at Figure 3.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;parsing_log.txt&#8217; in case the parsing process fails. If the process is completed successfully, the debugging messages will be printed out in the log file.</li>
</ol>
4. Mesh to PMID mapping
<ol>
	<li>Make sure that parsed data (&#8216;pubmed.json&#8217;) is available at the &#8216;data&#8217; directory.</li>
	<li>Type &#8216;python run_mesh2pmid.py&#8217; in the terminal to perform MeSH to PMID mapping. This creates a mapping table where each of the MeSH collects associated PMIDs. A Single PMID may fall under the multiple MeSH terms.</li>
	<li>Once the mapping is completed, make sure that there is &#8216;mesh2pmid.json&#8217; in the data directory. A sample of the top 20 mapping statistics is available in Table-2, Figures 4 and 5.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;mesh2pmid_mapping_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the mapping will be printed out in this log file.</li>
</ol>
5. Document indexing
<ol>
	<li>Download the Elasticsearch application from <a href="https://www.elastic.co/downloads">https://www.elastic.co</a>. Currently, the download is available at (<a href="https://www.elastic.co/downloads/elasticsearch">https://www.elastic.co/downloads/elasticsearch</a>). To download the software in the remote cloud, type &#8216;wget <a href="https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-x.x.x.tar.gz">https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-x.x.x.tar.gz</a>&#8217; in the terminal. Make sure that &#8216;x.x.x&#8217; in the above command is replaced by proper version number.</li>
	<li>Make sure that downloaded &#8216;elasticsearch-x.x.x.tar.gz&#8217; file appears in the root directory then extract the files by typing &#8216;tar xvzf elasticsearch-x.x.x.tar.gz&#8217; in the terminal window.</li>
	<li>Open a new terminal and go to the ElasticSearch bin directory by typing &#8216;cd Elasticsearch/bin&#8217; in the terminal from the root directory.</li>
	<li>Start the Elasticsearch server by typing &#8216;./Elasticsearch&#8217; in the terminal window. Make sure that the server is started without error messages. In case of error on starting Elasticsearch server, follow the instructions at (<a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html">https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html</a>).</li>
	<li>Modify the contents in the &#8216;index_init_config.json&#8217; in &#8216;config&#8217; directory to set index initiation. By default, it will select all items present.</li>
	<li>Type &#8216;python run_index_init.py&#8217; in the terminal to initiate an index-database in the Elasticsearch server. This initializes the index with a set of criteria known as index information (e.g., index name, type name, number of shards, number of replicas). You will see the message mentioning index is successfully created.</li>
	<li>Select the items in the &#8216;index_populate_config.json&#8217; in the &#8216;config&#8217; directory by setting their value to &#8216;true&#8217;. By default, it will select all items present.</li>
	<li>Make sure that parsed data (&#8216;pubmed.json&#8217;) is present in the &#8216;data&#8217; directory.</li>
	<li>Type &#8216;python run_index_populate.py&#8217; in the terminal to populate the index by creating bulk data with two components.A first component is a dictionary with metadata information on the index name, type name, and bulk id (e.g., &#8216;PMID&#8217;). A second component is a data dictionary containing all the information on the tags (e.g., &#8216;title&#8217;,&#8217;abstract&#8217;,&#8217;MeSH&#8217;). </li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;indexing_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the indexing will be printed out in the log file.</li>
</ol>
6. Text-cube creation
<ol>
	<li>Download the latest MeSH Tree available at (<a href="https://www.nlm.nih.gov/mesh/filelist.html">https://www.nlm.nih.gov/mesh/filelist.html</a>). The current version of the code is using MeSH Tree 2018 as &#8216;meshtree2018.bin&#8217; in the input directory.</li>
	<li>Define the categories of interest (e.g., Disease names, Age groups, Gender). A category may include one or more MeSH descriptors (<a href="https://meshb-prev.nlm.nih.gov/treeView">https://meshb-prev.nlm.nih.gov/treeView</a>). Collect MeSH IDs for a category. Save the names of the categories in the file &#8216;textcube_config.json&#8217; in the config directory (see a sample of the category in &#8216;Age Group&#8217; in the downloaded version of &#8216;textcube_config.json&#8217; file).</li>
	<li>Put the collected categories of MeSH IDs in a line separated by a space. Save the category file as &#8216;categories.txt&#8217; in the &#8216;input&#8217; directory (see a sample of &#8216;Age Group&#8217; MeSH IDs in the downloaded version of &#8216;categories.txt&#8217; file). This algorithm automatically selects all descendent MeSH descriptors. An example of root nodes and descendants are presented in Figure 4.</li>
	<li>Make sure that &#8216;mesh2pmid.json&#8217; is in the &#8216;data&#8217; directory. If the MeSH Tree has been updated with a different name (e.g., &#8216;meashtree2019.bin&#8217;) in &#8216;input&#8217; directory, make sure that this is properly represented in the input data path in the &#8216;run_textube.py&#8217; file.</li>
	<li>Type &#8216;python run_textcube.py&#8217; in the terminal to create a document data structure called Text-Cube. This creates a collection of documents (PMIDs) for each category. A single document (PMID) may fall under multiple categories, (see Table 3A, Table 3B, Figure 6A and Figure 7A).</li>
	<li>Once Text-Cube creation step is completed, make sure that following data files are saved in the &#8216;data&#8217; directory: (1) a cell to PMID table as &#8220;textcube_cell2pmid.json&#8221;, (2) a PMID to cell mapping table as &#8220;textcube_pmid2cell.json&#8221;, (3) a collection of all descendant MeSH terms for a cell as &#8220;meshterms_per_cat.json&#8221; (4) Text-Cube data statistics as &#8220;textcube_stat.txt&#8221;.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;textcube_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the Text-Cube creation will be printed out in the log file.</li>
</ol>
7. Entity count
<ol>
	<li>Create user-defined entities (e.g., protein names, genes, chemicals). Put one entity and its abbreviations in a single line separated by &#8220;|&#8221;. Save the entity file as &#8216;entities.txt&#8217; in the &#8216;input&#8217; directory. A sample of entities can be found in Table 4.</li>
	<li>Make sure that Elasticsearch server is running. Otherwise, go to step 5.2 and 5.3 to restart the Elasticsearch server. It is expected to have an indexed database called &#8216;pubmed&#8217; in your Elasticsearch server which was established in step 5.</li>
	<li>Make sure that &#8216;textcube_pmid2cell.json&#8217; is in the &#8216;data&#8217; directory.</li>
	<li>Type &#8216;python run_entitycount.py&#8217; in the terminal to perform Entity Count operation. This searches the documents from the indexed database and counts the entity in each document as well as collects the PMIDs in which entities were found.</li>
	<li>Once the Entity Count is completed, make sure that the final results are saved as &#8216;entitycount.txt&#8217; and &#8216;entityfound_pmid2cell.json&#8217; in the &#8216;data&#8217; directory.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;entitycount_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the Entity Count will be printed out in the log file.</li>
</ol>
8. Metadata update
<ol>
	<li>Make sure that all input data (&#8216;entitycount.txt&#8217;, &#8216;textcube_pmid2cell.json&#8217;, &#8216;entityfound_pmid2cell.txt&#8217;) are in the &#8216;data&#8217; directory. These are the input data for Metadata Update.</li>
	<li>Type &#8216;python run_metadata_update.py&#8217; in the terminal to update the metadata. This prepares a collection of metadata (e.g., cell name, associated MeSH, PMIDs) representing each text document in the cell. A sample of Text-Cube metadata is presented in Table 3A and Table 3B.</li>
	<li>Once the Metadata Update is completed, make sure that &#8216;metadata_pmid2pcount.json&#8217; and &#8216;metadata_cell2pmid.json&#8217; files are saved in &#8216;data&#8217; directory.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;metadata_update_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the metadata update will be printed out in the log file.</li>
</ol>
9. CaseOLAP score calculation
<ol>
	<li>Make sure that &#8216;metadata_pmid2pcount.json&#8217; and &#8216;metadata_cell2pmid.json&#8217; files are present in the &#8216;data&#8217; directory. These are the input data for score calculation.</li>
	<li>Type &#8216;python run_caseolap_score.py&#8217; in the terminal to perform CaseOLAP score calculation. This calculates the CaseOLAP score of the entities based on user-defined categories. The CaseOLAP score is the product of Integrity, Popularity, and Distinctiveness.</li>
	<li>Once the score computation is completed, make sure that this saves the results in multiple files (e.g., popularity as &#8216;pop.csv&#8217;, distinctiveness as &#8216;dist.csv&#8217;, CaseOLAP score as &#8217;caseolap.csv&#8217;), in the &#8216;result&#8217; directory. The summary of the CaseOLAP score calculation is also presented in Table 5.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;caseolap_score_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the CaseOLAP score calculation will be printed out in the log file.</li>
</ol>

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The authors have nothing to disclose.

We have demonstrated that the CaseOLAP algorithm can create a phrase based quantitative association to a knowledge-based category over large volumes of textual data for extraction of meaningful insights. Following our protocol, one can build the CaseOLAP framework to create a desired Text-Cube and quantify entity-category associations through CaseOLAP score calculation. The obtained raw CaseOLAP scores can be taken to integrative analyses including dimensionality reduction, clustering, temporal and geographical analysis, as well as the creation of a graphical database which enables semantic mapping of the documents.
Applicability of the algorithm. Examples of user-defined entities, other than proteins, could be a list of gene names, drugs, specific signs and symptoms including their abbreviations and synonyms. Furthermore, there are many choices for category selection to facilitate specific user-defined biomedical analyses (e.g., Anatomy [A], Discipline and Occupation [H], Phenomena and Processes [G]). In our two use cases, all scientific publications and their textual data are retrieved from the MEDLINE database using PubMed as the search engine, both managed by the National Library of Medicine. However, the CaseOLAP platform may be applied to other databases of interest containing biomedical documents with textual data such as the FDA Adverse Event Reporting System (FAERS). This is an open database containing information on medical adverse events and medication error reports submitted to FDA. In contrast to MEDLINE and FAERS, databases in hospitals containing electronic health records from patients are not open to the public and are restricted by the Health Insurance Portability and Accountability Act known as HIPAA.
CaseOLAP algorithm has been successfully applied to the different types of data (e.g., news articles)1. The implementation of this algorithm in biomedical documents has been made in 20185. The requirements for applicability of CaseOLAP algorithm is that each of the documents should be assigned with keywords associated with the concepts (e.g., MeSH descriptors in biomedical publications, keywords in news articles). If keywords are not found, one can apply Autophrase6,7&#160;to collect top representative phrases and build the entity list before implementing our protocol. Our protocol does not provide the step to perform Autophrase.
Comparison with other algorithms. The concept of using a Data-Cube8,9,10 and a Text-Cube2,3,4 has been evolving since 2005 with new advancements to make data mining more applicable. The concept of Online Analytical Processing (OLAP)11,12,13,14,15 in data mining and business intelligence goes back to 1993. OLAP, in general, aggregates the information from multiple systems, and stores it in a multi-dimensional format. There are different types of OLAP systems implemented in data mining. For example (1) Hybrid Transaction/Analytical Processing (HTAP)16,17, (2) Multidimensional OLAP (MOLAP)18,19-Cube based, and (3) Relational OLAP (ROLAP)20.
Specifically, the CaseOLAP algorithm has been compared with numerous existing algorithms, specifically, with their phrase segmentation enhancements, including TF-IDF+Seg, MCX+Seg, MCX, and SegPhrase. Moreover, RepPhrase (RP, also known as SegPhrase+) has been compared with its own ablation variations, including (1) RP without the Integrity measure incorporated (RP No INT), (2) RP without the Popularity measure incorporated (RP No POP), and (3) RP without the Distinctiveness measure incorporated (RP No DIS). The benchmark results are shown in the study by Fangbo Tao et al.1.
There are still challenges on data mining which can add additional functionality over saving and retrieving the data from the database. Context-aware semantic Analytical Processing (CaseOLAP) systematically implements the Elasticsearch to build an indexing database of millions of documents (Protocol 5). The Text-Cube is a document structure built over the indexed data with user-provided categories (Protocol 6). This enhances the functionality to the documents within and across the cell of the Text-Cube and allow us to calculate term frequency of the entities over a document and document frequency over a specific cell (Protocol 8). The final CaseOLAP score utilizes these frequency calculations to output a final score (Protocol 9). In 2018, we implemented this algorithm to study ECM proteins and six heart diseases to analyze protein-disease associations. The details of this study can be found in the study by Liem, D.A. et al.5. indicating that CaseOLAP could be widely used in the biomedical community exploring a variety of diseases and mechanisms.
Limitations of the algorithm. Phrase mining itself is a technique to manage and retrieve important concepts from textual data. While discovering entity-category association as a mathematical quantity (vector), this technique is unable to figure out the polarity (e.g., positive or negative inclination) of the association. One can build the quantitative summarization of the data utilizing the Text-Cude document structure with assigned entities and categories, but a qualitative concept with microscopic granularities cannot be reached. Some concepts are continuously evolving from past till now. The summarization presented for a specific entity-category association includes all incidences throughout the literature. This may lack the temporal propagation of the innovation. In the future, we plan to address these limitations.
Future Applications. About 90% of the accumulated data in the world is in the unstructured text data. Finding a representative phrase and relation to the entities embedded in the text is a very important task for the implementation of new technologies (e.g., Machine Learning, Information Extraction, Artificial Intelligence). To make the text-data machine readable, data need to be organized in the database over which the next layer of tools could be implemented. In the future, this algorithm can be a crucial step in making data mining more functional for the retrieval of information and the quantification of the entity-category associations.

Manual evaluation of millions of text files for the study of phrase-category association (e.g., Age group to protein association) is incomparable with the efficiency provided by an automated computational method. We want to introduce the cloud-based Context-aware Semantic Online Analytical Processing (CaseOLAP) platform as a phrase-mining method for automated computation of phrase-category association in the biomedical context.
The CaseOLAP platform, which was first defined in 20161, is very efficient compared to the traditional methods of data management and computation because of its functional document management called Text-Cube2,3,4,&#160;which distributes the documents while maintaining underlying hierarchy and neighbourhoods. It has been applied in biomedical research5&#160;to study entity-category association. The CaseOLAP platform consists of six major steps including download and extraction of data, parsing, indexing, Text-Cube creation, entity count, and CaseOLAP score calculation; which is the main focus of the protocol&#160;(Figure 1, Figure 2, Table 1).
To implement the CaseOLAP algorithm, the user sets up categories of interest (e.g., disease, signs and symptoms, age groups, diagnosis) and entities of interest (e.g., proteins, drugs). One example of a category included in this article is the &#39;Age Groups&#39;, which has &#39;infant&#39;, &#39;child&#39;, &#39;adolescent&#39;, and &#39;adult&#39; subcategories as cells of the Text-Cube and protein names (synonyms) and abbreviations as entities. Medical Subject Headings (MeSH) are implemented to retrieve publications corresponding to the defined categories (Table 2). MeSH descriptors are organized in a hierarchical tree structure to permit search for publications at varying levels of specificity&#160;(a sample shown in Figure 3). The CaseOLAP platform utilizes the data indexing and search functionality for curation of the documents associated with an entity which further facilitate document to entity count mapping and CaseOLAP score calculation.
The details of the CaseOLAP score calculation is available in previous publications1,5. This score is computed using specific ranking criteria based on underlying Text-Cube document structure. The final score is the product of Integrity, Popularity, and Distinctiveness. Integrity describes whether a representative entity is an integral semantic unit that collectively refers to a meaningful concept. The integrity of the user-defined phrase is taken to be 1.0 because it stands as a standard phrase in the literature. Distinctiveness represents the relative relevance of a phrase in one subset of documents compared to the rest of the other cells. It first calculates the relevance of an entity to a specific cell by comparing the occurrence of the protein name in the target data set and provides a normalized Distinctiveness score. Popularity represents the fact that phrase with a higher popularity score appears more frequently in one subset of documents. Rare protein names in a cell are ranked low, while an increase in their frequency of mention has a diminishing return due to the implementation of the logarithmic function of frequency. Quantitatively measuring these three concepts depends on the (1) term frequency of the entity over a cell and across the cells and (2) number of documents having that entity (document frequency) within the cell and across the cells.
We have studied two representative scenarios using a PubMed dataset and our algorithm. We are interested in how mitochondrial proteins are associated with two unique categories of MeSH descriptors; &#34;Age Groups&#34; and &#34;Nutritional and Metabolic Diseases&#34;. Specifically, we retrieved 15,728,250 publications from 20 years publications collected by PubMed (1998 to 2018), among them, 8,123,458 unique abstracts have had full MeSH descriptors. Accordingly, 1,842 human mitochondrial protein names (including abbreviations and synonyms), acquired from UniProt (uniprot.org) as well as from MitoCarta2.0 (http://mitominer.mrc-mbu.cam.ac.uk/release-4.0/begin.do&#62;), are systematically examined. Their associations with these 8,899,019 publications and entities were studied using our protocol; we constructed a Text-Cube and calculated the respective CaseOLAP scores.

cloud-based phrase mining and analysis of user-defined phrase-category association in biomedical publications

The rapid accumulation 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. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications.
We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, 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.
Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents.
CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

Watch this Scientific Journal Video about Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications at JoVE.com

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining ...

cloud-based-phrase-mining-analysis-user-defined-phrase-category

Research

JoVE Journal

Medicine

8.6K Views.  University of California, Los Angeles. Ons protocol biedt een stapsgewijze maatregel voor het bouwen van een cloudgebaseerd phrase mining-platform voor door de gebruiker gedefinieerde entiteitscategoriekoppeling, om de associatie van eiwitten, genomen of chemische stoffen met specifieke ziekten te evalueren. De belangrijkste voordelen van deze techniek zijn de verbeterde efficiëntie ten opzichte van handmatige entiteit categorie vereniging evaluatie, verbeterde toegankelijkheid en het gebruik van phrase mining tools voor wijdverb...

Video: Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating investigators to analyze and interpret genes, samples and clinical data across different types of cancer. Herein, we describe a practical operation procedure, taking ID1 (Inhibitor of DNA binding proteins 1) as an example, to characterize the expression patterns of biomarker and survival predictors of breast cancer based on pooled clinical datasets derived from online accessible databases, including ONCOMINE, bcGenExMiner v4.0 (Breast cancer gene-expression miner v4.0), GOBO (Gene expression-based Outcome for Breast cancer Online), HPA (The human protein atlas), and Kaplan-Meier plotter. The analysis began with querying the expression pattern of the gene of interest (e.g., ID1) in cancerous samples vs. normal samples. Then, the correlation analysis between ID1 and clinicopathological characteristics in breast cancer was performed. Next, the expression profiles of ID1 was stratified according to different subgroups. Finally, the association between ID1 expression and survival outcome was analyzed. The operation procedure simplifies the concept to integrate multidimensional data types at the gene level from different databases and test hypotheses regarding recurrence and genomic context of gene alteration events in breast cancer. This method can improve the credibility and representativeness of the conclusions, thereby, present informative perspective on a gene of interest.

Here, we present a protocol to explore the biomarker and survival predictor of breast cancer based on the comprehensive analysis of pooled clinical datasets derived from a variety of publicly accessible databases, using the strategy of expression, correlation and survival analysis step by step.

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating ...

Cancer Research

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are expected processes in many metagenomic data analyses. Since metagenomic data contain a large number of novel species and genes, high-performing classification algorithms are needed in many studies. Biologists often encounter challenges in finding suitable sequence classification and annotation tools for a specific task and are often not able to construct a corresponding algorithm on their own because of a lack of the necessary mathematical and computational knowledge. Deep learning techniques have recently become a popular topic and show strong advantages in many classification tasks. To date, many highly packaged deep learning packages, which make it possible for biologists to construct deep learning frameworks according to their own needs without in-depth knowledge of the algorithm details, have been developed. In this tutorial, we provide a guideline for constructing an easy-to-use deep learning framework for sequence classification without the need for sufficient mathematical knowledge or programming skills. All the code is optimized in a virtual machine so that users can directly run the code using their own data.

This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic data.

A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are ...

Genetics

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.g., biomedical literature). The software identifies sub-cellular proteins and their functional partners within disease-relevant documents. Additional disease-relevant documents are identified via the software's label imputation method. To contextualize the resulting protein-disease associations and to integrate information from multiple relevant biomedical resources, a knowledge graph is automatically constructed for further analyses. We present one use case with a corpus of ~34 million text documents downloaded online to provide an example of elucidating the role of mitochondrial proteins in distinct cardiovascular disease phenotypes using this method. Furthermore, a deep learning model was applied to the resulting knowledge graph to predict previously unreported relationships between proteins and disease, resulting in 1,583 associations with predicted probabilities &gt;0.90 and with an area under the receiver operating characteristic curve (AUROC) of 0.91 on the test set. This software features a highly customizable and automated workflow, with a broad scope of raw data available for analysis; therefore, using this method, protein-disease associations can be identified with enhanced reliability within a text corpus.

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for ...

Biochemistry

High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

The molecular synaptic mechanisms underlying auditory learning and memory remain largely unknown. Here, the workflow of a proteomic study on auditory discrimination learning in mice is described. In this learning paradigm, mice are trained in a shuttle box Go/NoGo-task to discriminate between rising and falling frequency-modulated tones in order to avoid a mild electric foot-shock. The protocol involves the enrichment of synaptosomes from four brain areas, namely the auditory cortex, frontal cortex, hippocampus, and striatum, at different stages of training. Synaptic protein expression patterns obtained from trained mice are compared to na&#239;ve controls using a proteomic approach. To achieve sufficient analytical depth, samples are fractionated in three different ways prior to mass spectrometry, namely 1D SDS-PAGE/in-gel digestion, in-solution digestion and phospho-peptide enrichment. 
High-resolution proteomic analysis on a mass spectrometer and label-free quantification are used to examine synaptic protein profiles in phospho-peptide-depleted and phospho-peptide-enriched fractions of synaptosomal protein samples. A commercial software package is utilized to reveal proteins and phospho-peptides with significantly regulated relative synaptic abundance levels (trained/na&#239;ve controls). Common and differential regulation modes for the synaptic proteome in the investigated brain regions of mice after training were observed. Subsequently, meta-analyses utilizing several databases are employed to identify underlying cellular functions and biological pathways.

The identification of molecules and pathways controlling synaptic plasticity and memory is still a major challenge in neuroscience. Here, a workflow is described addressing the relative quantification of synaptic proteins supposedly involved in the molecular reorganization of synapses during learning and memory consolidation in an auditory learning paradigm.

The molecular synaptic mechanisms underlying auditory learning and memory remain largely unknown. Here, the workflow of a proteomic study on auditory ...

Neuroscience

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data.

We present a protocol and associated metadata template for the extraction of text describing biomedical concepts in clinical case reports. The structured text values produced through this protocol can support deep analysis of thousands of clinical narratives.

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content ...

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly on life sciences and biomedical disciplines, whereas Scopus and Web of Science are multidisciplinary. The protocol described in the current study was used to search for publications from Jordanian authors in the years 2013-2017. In this protocol, how to use each database to conduct this type of search is explained in detail. A Scopus search resulted in the highest number of documents (11,444 documents), followed by a Web of Science search (10,943 documents). PubMed resulted in a smaller number of documents due to its narrower scope and coverage (4,363 documents). The results also show a yearly trend in: (1) the number of publications, (2) the disciplines that have the most publications, (3) the countries of collaboration, and (4) the number of open access publications. In contrast, PubMed has a sophisticated keyword optimization service (i.e., Medical Subject Heading, or MeSH), while both Scopus and Web of Science provide search analysis tools that can produce representative figures. Finally, the features of each database are explained in detail and several indices that can be extracted using the search results are provided. This study provides a base for using literature databases for bibliometric analysis.

Literature databases are commonly used to assess publications in a certain subject, discipline, country, or region of the world, a practice known as bibliometric analysis. The current protocol details how to use PubMed, Scopus, and Web of Science databases to do bibliometric analysis.

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly ...

Galaxy-Enabled Metaproteomics: Insights into Host-Microbiome Interaction

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the ...

Immunology and Infection

A Web Tool for Generating High Quality Machine-readable Biological Pathways

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway &#34;crowd sourcing&#34; means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be &#34;re-styled&#34; into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to generate over 2,000 pathway diagrams, which are now found in many online databases including HMDB, DrugBank, SMPDB, and ECMDB.

PathWhiz is a comprehensive, online pathway drawing tool for generating biochemical and biological pathways. It uses publicly accessible databases and easily expandable palettes consisting of pre-drawn pathway components. This protocol describes how to easily build new pathways, replicate and edit existing pathways, and propagate previously drawn pathways to different organisms.

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological ...

Biology

Sex-Specific Glial Biomarkers in Alzheimer&#39;s Disease: A Transcriptome Analysis

Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study analyzed 220,095 single-nuclei transcriptomes from the frontal cortex of thirty-three AD individuals in the GEO database. Sex-specific Differentially Expressed Genes (DEGs) were identified in glial cells, including 243 in astrocytes, 1,154 in microglia, and 572 in oligodendrocytes. Gene Ontology (GO) functional annotation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed functional concentration in synaptic, neural, and hormone-related pathways. Protein-protein interaction network (PPI) identified MT3, CALM2, DLG2, KCND2, PAKACB, CAMK2D, and NLGN4Y in astrocytes, TREM2, FOS, APOE, APP, and NLGN4Y in microglia, and GRIN2A, ITPR2, GNAS, and NLGN4Y in oligodendrocytes as key genes. NLGN4Y was the only gene shared by the three glia and was identified as the biomarker for the gender specificity of AD. Gene-transcription factor (TF)-miRNA coregulatory network identified key regulators for NLGN4Y and its target TCMs. Ecklonia kurome Okam (Kunbu) and Herba Ephedrae (Mahuang) were identified, and the effects of the active ingredients on AD were displayed. Finally, enrichment analysis of Kunbu and Mahuang suggested that they might act as therapeutic candidates for gender specificity of AD.

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer&#39;s Disease

This study analyzed single-nuclei transcriptomes of thirty-three individuals with Alzheimer's disease (AD), revealing sex-specific DEGs in glial cells. Functional enrichment analysis highlighted synaptic, neural, and hormone-related pathways. Key genes, namely NLGN4Y and its regulators, were identified, and potential therapeutic candidates for gender-specific AD were proposed.

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study ...

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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