Gene to Pubmed
Created: 2011-02-08 13:04:06
Last updated: 2011-02-10 16:01:41
Download WorkflowThis workflow takes in a list of gene and pathway names and searches the PubMed database for corresponding articles. Any matches to the genes are then retrieved (abstracts only). These abstracts are then returned to the user. The end result is the calculation of cosine vector space scores between gene/pathway and phenotype corpora. The workflow then removes any null values from the output. this is some extra text vbeing added It also counts the number of articles in the pubmed database in which each term occurs, and identifies the total number of articles in the entire PubMed database. It also identified the total number of articles within pubmed so that a term enrichment score may be calculated. The workflow also takes in a document containing abstracts that are related to a particular phenotype. Scientiifc terms are then extracted from this text and given a weighting according to the number of terms that appear in the document. The higher the value the better the score. This is given as: X = log((a / b) / (c / d)) where: a = number of occurnaces of individual terms in phenotype corpus b = number of abstracts in entire phenotype corpus c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed Once this has been created, the pathways obtained from the QTL and microarray pathway analysis workflows are analysed. The documents from a search of each pathway in pubmed are merged into a single document of pathway abstracts. The (unweighted) phenotype terms are then searched in the pathways corpus. This will determine if the phenotype term is listed with the given pathway. The higher the value the better the score. Each term is then assigned a weight as: Y = log((e / f) / (c /d)) where: a = number of occurnaces of individual terms in pathway corpus b = number of abstracts in pathway corpus (per pathway) c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed The weighted terms are then given a link score. This is the total of: X + Y. This gives the link between the pathway and the phenotype a score / significance value. The higher the score the more "appropriate/interesting" the link between the pathway and the phenotype. The terms are also ranked according to the number of pathways which have been given a weight. This is calculated as: W = Sum( X + Y). The higher the value the better the score.
Preview
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Run this Workflow in the Taverna Workbench...
Option 1:
Copy and paste this link into File > 'Open workflow location...'
http://www.myexperiment.org/workflows/1842/download?version=3
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Replace http:// in the link above with http://yourusername:yourpassword@
Workflow Components
Authors (1)
| Paul Fisher |
Titles (1)
| Gene to Pubmed |
Descriptions (3)
| This workflow takes in a list of gene names and searches the PubMed database for corresponding articles. Any matches to the genes are then retrieved (abstracts only). These abstracts are then returned to the user. |
| This workflow takes in a list of gene and pathway names and searches the PubMed database for corresponding articles. Any matches to the genes are then retrieved (abstracts only). These abstracts are then returned to the user. This workflow also takes in a phenotype search term, and searches for abstracts in the PubMed database. These are passed to the eSearch function and searched for in PubMed. Those abstracts found are returned to the user The end result is the calculation of cosine vector space scores between gene/pathway and phenotype corpora. The workflow then removes any null values from the output. this is some extra text vbeing added It also counts the number of articles in the pubmed database in which each term occurs, and identifies the total number of articles in the entire PubMed database. It also identified the total number of articles within pubmed so that a term enrichment score may be calculated. The workflow also takes in a document containing abstracts that are related to a particular phenotype. Scientiifc terms are then extracted from this text and given a weighting according to the number of terms that appear in the document. The higher the value the better the score. This is given as: X = log((a / b) / (c / d)) where: a = number of occurnaces of individual terms in phenotype corpus b = number of abstracts in entire phenotype corpus c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed Once this has been created, the pathways obtained from the QTL and microarray pathway analysis workflows are analysed. The documents from a search of each pathway in pubmed are merged into a single document of pathway abstracts. The (unweighted) phenotype terms are then searched in the pathways corpus. This will determine if the phenotype term is listed with the given pathway. The higher the value the better the score. Each term is then assigned a weight as: Y = log((e / f) / (c /d)) where: a = number of occurnaces of individual terms in pathway corpus b = number of abstracts in pathway corpus (per pathway) c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed The weighted terms are then given a link score. This is the total of: X + Y. This gives the link between the pathway and the phenotype a score / significance value. The higher the score the more "appropriate/interesting" the link between the pathway and the phenotype. The terms are also ranked according to the number of pathways which have been given a weight. This is calculated as: W = Sum( X + Y). The higher the value the better the score. |
| This workflow takes in a list of gene and pathway names and searches the PubMed database for corresponding articles. Any matches to the genes are then retrieved (abstracts only). These abstracts are then returned to the user. The end result is the calculation of cosine vector space scores between gene/pathway and phenotype corpora. The workflow then removes any null values from the output. this is some extra text vbeing added It also counts the number of articles in the pubmed database in which each term occurs, and identifies the total number of articles in the entire PubMed database. It also identified the total number of articles within pubmed so that a term enrichment score may be calculated. The workflow also takes in a document containing abstracts that are related to a particular phenotype. Scientiifc terms are then extracted from this text and given a weighting according to the number of terms that appear in the document. The higher the value the better the score. This is given as: X = log((a / b) / (c / d)) where: a = number of occurnaces of individual terms in phenotype corpus b = number of abstracts in entire phenotype corpus c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed Once this has been created, the pathways obtained from the QTL and microarray pathway analysis workflows are analysed. The documents from a search of each pathway in pubmed are merged into a single document of pathway abstracts. The (unweighted) phenotype terms are then searched in the pathways corpus. This will determine if the phenotype term is listed with the given pathway. The higher the value the better the score. Each term is then assigned a weight as: Y = log((e / f) / (c /d)) where: a = number of occurnaces of individual terms in pathway corpus b = number of abstracts in pathway corpus (per pathway) c = number of occurnaces of individual terms in entire pubmed d = number of articles in entire pubmed The weighted terms are then given a link score. This is the total of: X + Y. This gives the link between the pathway and the phenotype a score / significance value. The higher the score the more "appropriate/interesting" the link between the pathway and the phenotype. The terms are also ranked according to the number of pathways which have been given a weight. This is calculated as: W = Sum( X + Y). The higher the value the better the score. |
Dependencies (0)
None
Inputs (3)
| Name | Description |
|---|---|
| gene_names | |
| Phenotype_search_term | |
| pathway_descriptions |
Processors (73)
| Name | Type | Description |
|---|---|---|
| regex | stringconstant |
Value\n |
| remove_nulls | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++){
if (!(split[i].equals("")))
{
nonEmpty.add(split[i].trim());
}
}
String[] non_empty = new String[nonEmpty.size()];
for (int i = 0; i < non_empty.length; i ++)
{
non_empty[i] = nonEmpty.elementAt(i);
}
String output = "";
for (int i = 0; i < non_empty.length; i++)
{
output = output + (String) (non_empty[i] + "\n");
} |
| gene_and_abstract | beanshell |
ScriptString[] split = abstracts.split("\n");
String pathway_name = pathway;
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++)
{
String trimmed = split[i].trim();
nonEmpty.add(trimmed);
}
String output = ">> " + pathway_name + "\n";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + "\n");
} |
| split_search_terms | localworker |
ScriptList split = new ArrayList();
if (!string.equals("")) {
String regexString = ",";
if (regex != void) {
regexString = regex;
}
String[] result = string.split(regexString);
for (int i = 0; i < result.length; i++) {
split.add(result[i]);
}
}
|
| merge_outputs_2 | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| Search_PubMed | workflow | |
| clean_text | workflow | |
| regular_expression | stringconstant |
Value\n |
| Remove_duplicate_genes | localworker |
ScriptList strippedlist = new ArrayList();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
if (strippedlist.contains(item) == false) {
strippedlist.add(item);
}
}
|
| split_gene_names | localworker |
ScriptList split = new ArrayList();
if (!string.equals("")) {
String regexString = ",";
if (regex != void) {
regexString = regex;
}
String[] result = string.split(regexString);
for (int i = 0; i < result.length; i++) {
split.add(result[i]);
}
}
|
| remove_Nulls_2 | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++){
if (!(split[i].equals("")))
{
nonEmpty.add(split[i].trim());
}
}
String[] non_empty = new String[nonEmpty.size()];
for (int i = 0; i < non_empty.length; i ++)
{
non_empty[i] = nonEmpty.elementAt(i);
}
String output = "";
for (int i = 0; i < non_empty.length; i++)
{
output = output + (String) (non_empty[i] + "\n");
} |
| Merge_string_list_to_string | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| xpath | stringconstant |
Value/*[local-name(.)='eSearchResult']/*[local-name(.)='IdList']/*[local-name(.)='Id'] |
| run_eSearch | wsdl |
Wsdlhttp://eutils.ncbi.nlm.nih.gov/entrez/eutils/soap/eutils.wsdlWsdl Operationrun_eSearch |
| min_date | stringconstant |
Value2000/01/01 |
| extractPMID | localworker |
Scriptimport org.dom4j.Document;
import org.dom4j.Node;
import org.dom4j.io.SAXReader;
SAXReader reader = new SAXReader(false);
reader.setIncludeInternalDTDDeclarations(false);
reader.setIncludeExternalDTDDeclarations(false);
Document document = reader.read(new StringReader(xmltext));
List nodelist = document.selectNodes(xpath);
// Process the elements in the nodelist
ArrayList outputList = new ArrayList();
ArrayList outputXmlList = new ArrayList();
String val = null;
String xmlVal = null;
for (Iterator iter = nodelist.iterator(); iter.hasNext();) {
Node element = (Node) iter.next();
xmlVal = element.asXML();
val = element.getStringValue();
if (val != null && !val.equals("")) {
outputList.add(val);
outputXmlList.add(xmlVal);
}
}
List nodelist=outputList;
List nodelistAsXML=outputXmlList; |
| remove_Nulls_4 | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++){
if (!(split[i].equals("")))
{
nonEmpty.add(split[i].trim());
}
}
String[] non_empty = new String[nonEmpty.size()];
for (int i = 0; i < non_empty.length; i ++)
{
non_empty[i] = nonEmpty.elementAt(i);
}
String output = "";
for (int i = 0; i < non_empty.length; i++)
{
output = output + (String) (non_empty[i] + "\n");
} |
| concat_abstract_ids | beanshell |
ScriptString id = id.trim();
String abstract_text = abstract_text.trim();
String[] abstract_array = abstract_text.split("\n");
String abstract_amended = "";
if(abstract_array.length > 1)
{
abstract_amended = abstract_array[0] + " ";
for(int i = 1; i < abstract_array.length; i++)
{
abstract_amended = abstract_amended + abstract_array[i] + " ";
}
}
else
{
abstract_amended = abstract_array[0];
}
String date_text = date_text.trim();
String output = "";
output = id + "\t" + date_text + "\t" + abstract_amended; |
| max_return_phenotype | stringconstant |
Value5000 |
| pubmed_database | stringconstant |
Valuepubmed |
| merge_phenotype_abstracts | beanshell |
ScriptString[] split = abstracts.split("\n");
String phenotype_term = phenotype.trim();
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++)
{
String trimmed = split[i].trim();
// String mytext = split[i].substring(split[i].indexOf(0), split[i].indexOf(" AND "));
nonEmpty.add(trimmed);
}
String output = ">> " + phenotype_term + "\n";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + "\n");
} |
| merge_abstracts | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| max_date | stringconstant |
Value2011/01/31 |
| parametersXML_eSearch | xmlsplitter | |
| merge_abstract_ids | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| merge_dates | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| Fetch_Abstracts | workflow | |
| clean_text_3 | workflow | |
| Encode_byte_to_base64 | localworker |
Scriptimport org.apache.commons.codec.binary.Base64; base64 = new String(Base64.encodeBase64(bytes)); |
| merge_strings_2 | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| regex_3 | stringconstant |
Value\n |
| xpath_2 | stringconstant |
Value/*[local-name(.)='generateTerminologyResponse']/*[local-name(.)='return'] |
| extract_abstracts | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 1; i < split.length; i++)
{
String trimmed = split[i].trim();
String[] split_2 = trimmed.split("\t");
if(split_2.length == 3)
{
nonEmpty.add(split_2[2]);
}
}
String output_search = split[0] + "\n";
String output = "";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + "\n");
} |
| merge_search_and_terms | beanshell |
ScriptString term_input = terms.trim(); String search_input = search_term.trim(); String output = ""; output = search_input + "\n" + term_input; |
| extract_Terms_2 | localworker |
Scriptimport org.dom4j.Document;
import org.dom4j.Node;
import org.dom4j.io.SAXReader;
SAXReader reader = new SAXReader(false);
reader.setIncludeInternalDTDDeclarations(false);
reader.setIncludeExternalDTDDeclarations(false);
Document document = reader.read(new StringReader(xmltext));
List nodelist = document.selectNodes(xpath);
// Process the elements in the nodelist
ArrayList outputList = new ArrayList();
ArrayList outputXmlList = new ArrayList();
String val = null;
String xmlVal = null;
for (Iterator iter = nodelist.iterator(); iter.hasNext();) {
Node element = (Node) iter.next();
xmlVal = element.asXML();
val = element.getStringValue();
if (val != null && !val.equals("")) {
outputList.add(val);
outputXmlList.add(xmlVal);
}
}
List nodelist=outputList;
List nodelistAsXML=outputXmlList; |
| merge_strings | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| remove_Nulls_5 | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++){
if (!(split[i].equals("")))
{
nonEmpty.add(split[i].trim());
}
}
String[] non_empty = new String[nonEmpty.size()];
for (int i = 0; i < non_empty.length; i ++)
{
non_empty[i] = nonEmpty.elementAt(i);
}
String output = "";
for (int i = 0; i < non_empty.length; i++)
{
output = output + (String) (non_empty[i] + "\n");
} |
| Remove_duplicate_strings_2 | localworker |
ScriptList strippedlist = new ArrayList();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
if (strippedlist.contains(item) == false) {
strippedlist.add(item);
}
}
|
| parametersXML | xmlsplitter | |
| split_by_regex | localworker |
ScriptList split = new ArrayList();
if (!string.equals("")) {
String regexString = ",";
if (regex != void) {
regexString = regex;
}
String[] result = string.split(regexString);
for (int i = 0; i < result.length; i++) {
split.add(result[i]);
}
}
|
| generateTerminology | wsdl |
Wsdlhttp://gopubmed4.biotec.tu-dresden.de/GoPubMedTermGenerationService/services/GoPubMedTermGeneration?wsdlWsdl OperationgenerateTerminology |
| clean_text_4 | soaplab |
Endpointhttp://phoebus.cs.man.ac.uk:1977/axis/services/text_mining.clean_text |
| pubmed_database_2 | stringconstant |
Valuepubmed |
| xpath_3 | stringconstant |
Value/*[local-name(.)='eSearchResult']/*[local-name(.)='Count'] |
| count | stringconstant |
Valuecount |
| xpath_count | stringconstant |
Value/*[local-name(.)='eInfoResult']/*[local-name(.)='DbInfo']/*[local-name(.)='Count'] |
| eSearch_database | stringconstant |
Valuepubmed |
| regular_expression_2 | stringconstant |
Value\n |
| extract_terms_3 | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 1; i < split.length; i++)
{
String trimmed = split[i].trim();
// if((trimmed.contains("=")) || (trimmed.contains("-")))
// {
// next;
// }
// else
// {
// String[] trimmed_array = trimmed.split("\t");
// String term = trimmed_array[0];
nonEmpty.add(trimmed);
// }
}
String output = "";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + "\n");
} |
| merge_term_count | beanshell |
ScriptString term_input = term.trim(); String count_input = count.trim(); String output = ""; output = term_input + "\t" + count_input; |
| split_extracted_terms | localworker |
ScriptList split = new ArrayList();
if (!string.equals("")) {
String regexString = ",";
if (regex != void) {
regexString = regex;
}
String[] result = string.split(regexString);
for (int i = 0; i < result.length; i++) {
split.add(result[i]);
}
}
|
| merge_pubmed_count | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| merge_extracted | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| merge_list | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| extractCount_2 | localworker |
Scriptimport org.dom4j.Document;
import org.dom4j.Node;
import org.dom4j.io.SAXReader;
SAXReader reader = new SAXReader(false);
reader.setIncludeInternalDTDDeclarations(false);
reader.setIncludeExternalDTDDeclarations(false);
Document document = reader.read(new StringReader(xmltext));
List nodelist = document.selectNodes(xpath);
// Process the elements in the nodelist
ArrayList outputList = new ArrayList();
ArrayList outputXmlList = new ArrayList();
String val = null;
String xmlVal = null;
for (Iterator iter = nodelist.iterator(); iter.hasNext();) {
Node element = (Node) iter.next();
xmlVal = element.asXML();
val = element.getStringValue();
if (val != null && !val.equals("")) {
outputList.add(val);
outputXmlList.add(xmlVal);
}
}
List nodelist=outputList;
List nodelistAsXML=outputXmlList; |
| extractCount | localworker |
Scriptimport org.dom4j.Document;
import org.dom4j.Node;
import org.dom4j.io.SAXReader;
SAXReader reader = new SAXReader(false);
reader.setIncludeInternalDTDDeclarations(false);
reader.setIncludeExternalDTDDeclarations(false);
Document document = reader.read(new StringReader(xmltext));
List nodelist = document.selectNodes(xpath);
// Process the elements in the nodelist
ArrayList outputList = new ArrayList();
ArrayList outputXmlList = new ArrayList();
String val = null;
String xmlVal = null;
for (Iterator iter = nodelist.iterator(); iter.hasNext();) {
Node element = (Node) iter.next();
xmlVal = element.asXML();
val = element.getStringValue();
if (val != null && !val.equals("")) {
outputList.add(val);
outputXmlList.add(xmlVal);
}
}
List nodelist=outputList;
List nodelistAsXML=outputXmlList; |
| parametersXML_1 | xmlsplitter | |
| run_eInfo | wsdl |
Wsdlhttp://eutils.ncbi.nlm.nih.gov/soap/v2.0/eutils.wsdlWsdl Operationrun_eInfo |
| run_eSearch_2 | wsdl |
Wsdlhttp://eutils.ncbi.nlm.nih.gov/soap/v2.0/eutils.wsdlWsdl Operationrun_eSearch |
| run_eSearch_request | xmlsplitter | |
| GENE_RankPhenotypeTerms | workflow | |
| max_return_gene_pathway | stringconstant |
Value500 |
| maxNumberOfTerms_value | stringconstant |
Value20000 |
| split_search_terms_2 | localworker |
ScriptList split = new ArrayList();
if (!string.equals("")) {
String regexString = ",";
if (regex != void) {
regexString = regex;
}
String[] result = string.split(regexString);
for (int i = 0; i < result.length; i++) {
split.add(result[i]);
}
}
|
| pathway_and_abstract | beanshell |
ScriptString[] split = abstracts.split("\n");
String pathway_name = pathway;
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++)
{
String trimmed = split[i].trim();
nonEmpty.add(trimmed);
}
String output = ">> " + pathway_name + "\n";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + "\n");
} |
| remove_nulls_3 | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++){
if (!(split[i].equals("")))
{
nonEmpty.add(split[i].trim());
}
}
String[] non_empty = new String[nonEmpty.size()];
for (int i = 0; i < non_empty.length; i ++)
{
non_empty[i] = nonEmpty.elementAt(i);
}
String output = "";
for (int i = 0; i < non_empty.length; i++)
{
output = output + (String) (non_empty[i] + "\n");
} |
| merge_outputs_2_2 | localworker |
ScriptString seperatorString = "\n";
if (seperator != void) {
seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
String item = (String) i.next();
sb.append(item);
if (i.hasNext()) {
sb.append(seperatorString);
}
}
concatenated = sb.toString();
|
| regex_2 | stringconstant |
Value\n |
| extract_terms | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++)
{
String mytext = split[i].substring(split[i].indexOf(" "), split[i].lastIndexOf(" - "));
nonEmpty.add(mytext);
}
String output = "";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + "\n");
} |
| add_MeSH_to_string | beanshell |
ScriptString[] split = input.split("\n");
Vector nonEmpty = new Vector();
for (int i = 0; i < split.length; i++)
{
String trimmed = split[i].trim();
nonEmpty.add(trimmed);
}
String output = "";
for (int i = 0; i < nonEmpty.size(); i++)
{
output = output + (String) (nonEmpty.elementAt(i) + " AND \"Metabolic Networks and Pathways\"[MeSH Terms]" + "\n");
} |
| Search_PubMed_2 | workflow | |
| clean_text_2 | workflow | |
| PATHWAY_RankPhenotypeTerms | workflow |
Beanshells (22)
| Name | Description | Inputs | Outputs |
|---|---|---|---|
| remove_nulls | input | output | |
| gene_and_abstract |
pathway abstracts |
output | |
| remove_Nulls_2 | input | output | |
| remove_Nulls_4 | input | output | |
| concat_abstract_ids |
id abstract_text date_text |
output | |
| merge_phenotype_abstracts |
phenotype abstracts |
output | |
| extract_abstracts | input |
output output_search |
|
| merge_search_and_terms |
terms search_term |
output | |
| remove_Nulls_5 | input | output | |
| extract_terms_3 | input | output | |
| merge_term_count |
term count |
output | |
| pathway_and_abstract |
pathway abstracts |
output | |
| remove_nulls_3 | input | output | |
| extract_terms | input | output | |
| add_MeSH_to_string | input | output | |
| format_rankings | ranked_terms | title_term_rankings | |
| remove_Nulls | input | output | |
| stringToBytes | string | bytes | |
| stringToBytes | string | bytes | |
| concat_abstract_ids |
id abstract_text date_text |
output | |
| concat_abstract_ids |
id abstract_text date_text |
output | |
| stringToBytes | string | bytes |
Outputs (10)
| Name | Description |
|---|---|
| gene_abstracts | |
| phenotype_abstracts | |
| phenotype_terms | |
| phenotype_term_counts | |
| pubmed_abstract_number | |
| gene_cosine | |
| gene_term_enrichment_scores | |
| pathway_abstracts | |
| pathway_cosine_vector_scores | |
| pathway_concept_rankings |
Datalinks (104)
| Source | Sink |
|---|---|
| gene_and_abstract:output | remove_nulls:input |
| Search_PubMed:concat_data | gene_and_abstract:abstracts |
| split_search_terms:split | gene_and_abstract:pathway |
| regex:value | split_search_terms:regex |
| remove_Nulls_2:output | split_search_terms:string |
| clean_text:cleaned_text | merge_outputs_2:stringlist |
| split_search_terms:split | Search_PubMed:search_term |
| min_date:value | Search_PubMed:min_date |
| max_date:value | Search_PubMed:max_date |
| max_return_gene_pathway:value | Search_PubMed:max_return |
| remove_nulls:output | clean_text:input |
| split_gene_names:split | Remove_duplicate_genes:stringlist |
| gene_names | split_gene_names:string |
| regular_expression:value | split_gene_names:regex |
| Merge_string_list_to_string:concatenated | remove_Nulls_2:input |
| Remove_duplicate_genes:strippedlist | Merge_string_list_to_string:stringlist |
| parametersXML_eSearch:output | run_eSearch:parameters |
| xpath:value | extractPMID:xpath |
| run_eSearch:parameters | extractPMID:xml-text |
| merge_phenotype_abstracts:output | remove_Nulls_4:input |
| extractPMID:nodelist | concat_abstract_ids:id |
| merge_abstracts:concatenated | concat_abstract_ids:abstract_text |
| merge_dates:concatenated | concat_abstract_ids:date_text |
| merge_abstract_ids:concatenated | merge_phenotype_abstracts:abstracts |
| Phenotype_search_term | merge_phenotype_abstracts:phenotype |
| Fetch_Abstracts:abstracts | merge_abstracts:stringlist |
| max_return_phenotype:value | parametersXML_eSearch:RetMax |
| pubmed_database:value | parametersXML_eSearch:db |
| max_date:value | parametersXML_eSearch:maxdate |
| min_date:value | parametersXML_eSearch:mindate |
| Phenotype_search_term | parametersXML_eSearch:term |
| concat_abstract_ids:output | merge_abstract_ids:stringlist |
| Fetch_Abstracts:pubmed_dates | merge_dates:stringlist |
| extractPMID:nodelist | Fetch_Abstracts:pubmed_ids |
| remove_Nulls_4:output | clean_text_3:input |
| clean_text_3:cleaned_text | Encode_byte_to_base64:bytes |
| merge_strings:concatenated | merge_strings_2:stringlist |
| clean_text_4:output | extract_abstracts:input |
| merge_strings_2:concatenated | merge_search_and_terms:terms |
| extract_abstracts:output_search | merge_search_and_terms:search_term |
| generateTerminology:parameters | extract_Terms_2:xml-text |
| xpath_2:value | extract_Terms_2:xpath |
| Remove_duplicate_strings_2:strippedlist | merge_strings:stringlist |
| merge_search_and_terms:output | remove_Nulls_5:input |
| extract_Terms_2:nodelist | Remove_duplicate_strings_2:stringlist |
| split_by_regex:split | parametersXML:texts |
| maxNumberOfTerms_value:value | parametersXML:maxNumberOfTerms |
| regex_3:value | split_by_regex:regex |
| extract_abstracts:output | split_by_regex:string |
| parametersXML:output | generateTerminology:parameters |
| Encode_byte_to_base64:base64 | clean_text_4:file_direct_data |
| remove_Nulls_5:output | extract_terms_3:input |
| split_extracted_terms:split | merge_term_count:term |
| merge_extracted:concatenated | merge_term_count:count |
| extract_terms_3:output | split_extracted_terms:string |
| regular_expression_2:value | split_extracted_terms:regex |
| extractCount:nodelist | merge_pubmed_count:stringlist |
| extractCount_2:nodelist | merge_extracted:stringlist |
| merge_term_count:output | merge_list:stringlist |
| xpath_3:value | extractCount_2:xpath |
| run_eSearch_2:result | extractCount_2:xml-text |
| xpath_count:value | extractCount:xpath |
| run_eInfo:result | extractCount:xml-text |
| pubmed_database_2:value | parametersXML_1:db |
| parametersXML_1:output | run_eInfo:request |
| run_eSearch_request:output | run_eSearch_2:request |
| eSearch_database:value | run_eSearch_request:db |
| count:value | run_eSearch_request:rettype |
| split_extracted_terms:split | run_eSearch_request:term |
| min_date:value | run_eSearch_request:mindate |
| max_date:value | run_eSearch_request:maxdate |
| merge_pubmed_count:concatenated | GENE_RankPhenotypeTerms:pubmed_abstract_number |
| merge_list:concatenated | GENE_RankPhenotypeTerms:phenotype_term_counts |
| merge_outputs_2:concatenated | GENE_RankPhenotypeTerms:query_abstracts |
| clean_text_3:cleaned_text | GENE_RankPhenotypeTerms:phenotype_abstracts |
| remove_Nulls_5:output | GENE_RankPhenotypeTerms:phenotype_terms |
| add_MeSH_to_string:output | split_search_terms_2:string |
| regex_2:value | split_search_terms_2:regex |
| Search_PubMed_2:concat_data | pathway_and_abstract:abstracts |
| split_search_terms_2:split | pathway_and_abstract:pathway |
| pathway_and_abstract:output | remove_nulls_3:input |
| clean_text_2:cleaned_text | merge_outputs_2_2:stringlist |
| pathway_descriptions | extract_terms:input |
| extract_terms:output | add_MeSH_to_string:input |
| split_search_terms_2:split | Search_PubMed_2:Pathway_search_term |
| min_date:value | Search_PubMed_2:min_date |
| max_date:value | Search_PubMed_2:max_date |
| max_return_gene_pathway:value | Search_PubMed_2:max_return |
| remove_nulls_3:output | clean_text_2:input |
| merge_outputs_2_2:concatenated | PATHWAY_RankPhenotypeTerms:query_abstracts |
| merge_pubmed_count:concatenated | PATHWAY_RankPhenotypeTerms:pubmed_abstract_number |
| merge_list:concatenated | PATHWAY_RankPhenotypeTerms:phenotype_term_counts |
| remove_Nulls_5:output | PATHWAY_RankPhenotypeTerms:phenotype_terms |
| clean_text_3:cleaned_text | PATHWAY_RankPhenotypeTerms:phenotype_abstracts |
| merge_outputs_2:concatenated | gene_abstracts |
| clean_text_3:cleaned_text | phenotype_abstracts |
| remove_Nulls_5:output | phenotype_terms |
| merge_list:concatenated | phenotype_term_counts |
| merge_pubmed_count:concatenated | pubmed_abstract_number |
| GENE_RankPhenotypeTerms:cosine_vector_scores | gene_cosine |
| GENE_RankPhenotypeTerms:concept_rankings | gene_term_enrichment_scores |
| merge_outputs_2_2:concatenated | pathway_abstracts |
| PATHWAY_RankPhenotypeTerms:cosine_vector_scores | pathway_cosine_vector_scores |
| PATHWAY_RankPhenotypeTerms:concept_rankings | pathway_concept_rankings |
Coordinations (0)
None
Workflow Type
Version 3
(of 4)
(People/Groups)
(Workflows/Files)
- abstracts
- |
- cosine vector space
- |
- data-driven
- |
- disease
- |
- enrichment
- |
- entity recognition
- |
- evidence
- |
- kegg
- |
- Kegg Pathways
- |
- literature
- |
- loci
- |
- locus
- |
- medline
- |
- microarray
- |
- mining
- |
- pathway
- |
- pathway-driven
- |
- pathways
- |
- phenotype
- |
- pubmed
- |
- qtl
- |
- ranking
- |
- re-use
- |
- term
- |
- term extraction
- |
- terms
- |
- text
- |
- text mining
- |
- text mining; term extraction; entity recognition
- |
- trichuris muris
- abstracts
- |
- cosine vector space
- |
- data-driven
- |
- disease
- |
- enrichment
- |
- entity recognition
- |
- evidence
- |
- kegg
- |
- Kegg Pathways
- |
- literature
- |
- loci
- |
- locus
- |
- medline
- |
- microarray
- |
- mining
- |
- pathway
- |
- pathway-driven
- |
- pathways
- |
- phenotype
- |
- pubmed
- |
- qtl
- |
- ranking
- |
- re-use
- |
- term
- |
- term extraction
- |
- terms
- |
- text
- |
- text mining
- |
- text mining; term extraction; entity recognition
- |
- trichuris muris
Log in to add Tags
Shared with Groups (0)
None
Statistics
Version History
In chronological order:
-
Created by Paul Fisher on Tuesday 08 February 2011 13:04:06 (UTC)
-
Created by Paul Fisher on Tuesday 08 February 2011 13:06:21 (UTC)
Last edited by Paul Fisher on Tuesday 08 February 2011 13:07:08 (UTC)
Revision comment:Created output ports for pathway cosine and concept rankings
-
Created by Paul Fisher on Tuesday 08 February 2011 13:10:57 (UTC)
Last edited by Paul Fisher on Tuesday 08 February 2011 13:12:01 (UTC)
Revision comment:Description of workflow updated - in Taverna
-
Created by Paul Fisher on Thursday 10 February 2011 16:00:56 (UTC)
Last edited by Paul Fisher on Thursday 10 February 2011 16:01:41 (UTC)
Revision comment:changed the term extraction service to point to new URL
Reviews
(0)
Other workflows that use similar services
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Pathway and Gene to Pubmed
(2)
Created: 2011-02-10 | Last updated: 2011-02-18
Credits:
Paul Fisher
Attributions:
Cosine vector space
Extract Scientific Terms
Rank Phenotype Terms
Cosine vector space
Rank Phenotype Terms
Pathway to Pubmed
Extract Scientific Terms
Gene to Pubmed
PubMed Search
(1)
Created: 2011-02-03 | Last updated: 2011-02-03
Credits:
Paul Fisher
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