Publication
High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning
| dc.contributor.author | Kandaswamy, C. | |
| dc.contributor.author | Silva, L. M. | |
| dc.contributor.author | Alexandre, L. A. | |
| dc.contributor.author | Santos, Jorge M. | |
| dc.date.accessioned | 2016-03-30T09:40:43Z | |
| dc.date.available | 2016-03-30T09:40:43Z | |
| dc.date.issued | 2016-03 | |
| dc.description.abstract | High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement. | pt_PT |
| dc.identifier.doi | 10.1177/1087057115623451 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/7965 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | SAGE | pt_PT |
| dc.relation | PTDC/ EIA-EIA/119004/2010 | pt_PT |
| dc.relation.ispartofseries | Journal of biomolecular screening;Vol. 21, nº3 | |
| dc.relation.publisherversion | http://jbx.sagepub.com/content/early/2015/12/31/1087057115623451.abstract | pt_PT |
| dc.subject | Cancer drug discovery | pt_PT |
| dc.subject | Deep transfer learning | pt_PT |
| dc.subject | High-content screening | pt_PT |
| dc.subject | Image analysis | pt_PT |
| dc.title | High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 21 | pt_PT |
| oaire.citation.startPage | 252 | pt_PT |
| oaire.citation.title | Journal of biomolecular screening | pt_PT |
| oaire.citation.volume | 3 | pt_PT |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |