| Chapter title |
Pathway mapping tools for analysis of high content data.
|
|---|---|
| Book title |
High Content Screening
|
| Published in |
Methods in molecular biology, September 2006
|
| DOI | 10.1385/1-59745-217-3:319 |
| Pubmed ID | |
| Book ISBNs |
978-1-58829-731-0, 978-1-59745-217-5
|
| Authors |
Ekins S, Nikolsky Y, Bugrim A, Kirillov E, Nikolskaya T, Sean Ekins, Yuri Nikolsky, Andrej Bugrim, Eugene Kirillov, Tatiana Nikolskaya, Ekins, Sean, Nikolsky, Yuri, Bugrim, Andrej, Kirillov, Eugene, Nikolskaya, Tatiana |
| Abstract |
The complexity of human biology requires a systems approach that uses computational approaches to integrate different data types. Systems biology encompasses the complete biological system of metabolic and signaling pathways, which can be assessed by measuring global gene expression, protein content, metabolic profiles, and individual genetic, clinical, and phenotypic data. High content screening assays can also be used to generate systems biology knowledge. In this review, we will summarize the pathway databases and describe biological network tools used predominantly with this genomics, proteomics, and metabolomics data but which are equally as applicable for high content screening data analysis. We describe in detail the integrated data-mining tools applicable to building biological networks developed by GeneGo, namely, MetaCore and MetaDrug. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 7% |
| Malaysia | 1 | 1% |
| Belgium | 1 | 1% |
| Netherlands | 1 | 1% |
| Russia | 1 | 1% |
| Luxembourg | 1 | 1% |
| Unknown | 88 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 25 | 25% |
| Student > Ph. D. Student | 23 | 23% |
| Student > Master | 11 | 11% |
| Professor | 6 | 6% |
| Professor > Associate Professor | 6 | 6% |
| Other | 19 | 19% |
| Unknown | 10 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 40 | 40% |
| Biochemistry, Genetics and Molecular Biology | 9 | 9% |
| Medicine and Dentistry | 9 | 9% |
| Computer Science | 7 | 7% |
| Chemistry | 3 | 3% |
| Other | 19 | 19% |
| Unknown | 13 | 13% |