RESUMO
Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdos-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.
Assuntos
Algoritmos , Antineoplásicos/uso terapêutico , Neoplasias da Mama/tratamento farmacológico , Reposicionamento de Medicamentos/métodos , Proteínas de Neoplasias/genética , Neoplasias Ovarianas/tratamento farmacológico , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Biologia Computacional/métodos , Feminino , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes/efeitos dos fármacos , Humanos , Terapia de Alvo Molecular , Proteínas de Neoplasias/antagonistas & inibidores , Proteínas de Neoplasias/metabolismo , Neoplasias Ovarianas/genética , Neoplasias Ovarianas/metabolismo , Neoplasias Ovarianas/patologia , Neoplasias Pancreáticas/genética , Neoplasias Pancreáticas/metabolismo , Neoplasias Pancreáticas/patologia , Medicamentos sob Prescrição/uso terapêutico , Mapas de Interação de Proteínas/efeitos dos fármacosRESUMO
BACKGROUND: Medical technology is far from reaching its full potential. An area that is currently expanding is that of precision medicine. The aim of this article is to present an application of precision medicine-a deep-learning approach to computer-aided diagnosis in the field of dermatology. MATERIALS AND METHODS: The main dataset was proposed in the edition of the ISIC Challenge that took place in 2019 and included 25 331 dermoscopic images from eight different categories of lesions-three of them were malignant and five benign. The behavior of the model was also tested on a dataset collected from the second Department of Dermatology, of the Colentina Clinical Hospital. RESULTS: The overall accuracy of the model was 78.11%. Of the total 5031 samples included in the test subset, 3958 were correctly classified. The accuracy of the model on the clinical dataset is lower than that obtained in the first instance. CONCLUSION: The architecture of the model can be considered of general use, being able to be adapted in an optimal way for a wide range of classifications. The model has achieved performance within the expected limits but can be further improved by new methods.
