Name
Abstract | ||
|---|---|---|
Understanding how organisms develop from a single cell into a functioning multicellular organism is one of the key questions in developmental biology. Research in this area goes back decades ago, but only recently have improvements in technology allowed biologists to achieve experimental results that are more quantitative and precise. Here, we show how large biological datasets can be used to learn a model for predicting the patterns of gene expression in Drosophila melanogaster (fruit fly) throughout embryogenesis. We also explore the possibility of considering spatial information in order to achieve unique patterns of gene expression in different regions along the anterior-posterior (head-tail) axis of the egg. We then demonstrate how the resulting model can be used to (1) classify these regions into the various segments of the fly, and (2) to conduct a virtual gene knockout experiment. Our learning algorithm is based on a model that has biological meaning, which indicates that its structure and parameters have their correspondence in biology. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2576768.2598250 | GECCO |
Keywords | Field | DocType |
gene expression,prediction,biology and genetics,parameter learning,gene regulatory networks,systems biology,local search,machine learning,bioinformatics,pattern recognition and classification | Drosophila embryogenesis,Multicellular organism,Computer science,Artificial intelligence,Computational biology,Gene knockout,Gene expression,Systems biology,Bioinformatics,Developmental biology,Drosophila melanogaster,Gene regulatory network,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rotem Golan | 1 | 0 | 0.34 |
| Christian Jacob | 2 | 211 | 33.00 |
| Savraj Grewal | 3 | 0 | 0.34 |
| Jörg Denzinger | 4 | 439 | 52.75 |