title: High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning.
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.
authors.affiliation: Instituto de Engenharia Biomédica (INEB), Porto, Portugal Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal chetak.kand@gmail.com.
authors.forename: Chetak
authors.initials: C
authors.lastname: Kandaswamy
authors.affiliation: Instituto de Engenharia Biomédica (INEB), Porto, Portugal Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal Departamento de Matemática, Universidade de Aveiro, Aveiro, Portugal.
authors.forename: Luís M
authors.initials: LM
authors.lastname: Silva
authors.affiliation: Universidade da Beira Interior, Instituto de Telecomunicações, Covilhã, Portugal.
authors.forename: Luís A
authors.initials: LA
authors.lastname: Alexandre
authors.affiliation: Instituto de Engenharia Biomédica (INEB), Porto, Portugal Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal Departamento de Matemática, Instituto Superior de Engenharia do Instituto Politécnico do Porto, Porto, Portugal.
authors.forename: Jorge M
authors.initials: JM
authors.lastname: Santos
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods; D055808:Drug Discovery / Q000379:methods*; D005260:Female; D005818:Genetic Engineering; D057166:High-Throughput Screening Assays; D006801:Humans; D010641:Phenotype; D015203:Reproducibility of Results; D054852:Small Molecule Libraries
chemical_list: D000970:Antineoplastic Agents; D054852:Small Molecule Libraries
keywords: cancer drug discovery; deep transfer learning; high-content screening; image analysis