| Chapter title |
Using ILP to Identify Pathway Activation Patterns in Systems Biology
|
|---|---|
| Chapter number | 10 |
| Book title |
Inductive Logic Programming
|
| Published in |
Inductive logic programming. ILP, August 2015
|
| DOI | 10.1007/978-3-319-40566-7_10 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-940565-0, 978-3-31-940566-7
|
| Authors |
Samuel R. Neaves, Louise A. C. Millard, Sophia Tsoka |
| Abstract |
We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 13% |
| Unknown | 7 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 38% |
| Other | 2 | 25% |
| Lecturer > Senior Lecturer | 1 | 13% |
| Student > Master | 1 | 13% |
| Student > Ph. D. Student | 1 | 13% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 50% |
| Philosophy | 1 | 13% |
| Mathematics | 1 | 13% |
| Engineering | 1 | 13% |
| Unknown | 1 | 13% |