OsteoCLUST: clusterização da resposta à biomateriais ósseos
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Data
2023-02-13
Autores
Silva, Matheus Amaral
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Universidade Estadual Paulista (Unesp)
Resumo
As lesões ósseas constituem um dos problemas mais comuns a serem considerados na odontologia e na medicina. A reconstrução de tecidos perdidos é um dos grandes desafios enfrentados pelos profissionais dessas áreas, exigindo muitas vezes a aplicação de biomateriais capazes de uma boa regeneração tecidual. Apesar da relevância clínica, social e econômica de tais biomateriais, pouco se avançou em metodologias de bioanálise capazes de hierarquizar novos biomateriais. Nos últimos 10 anos, LaBIO, coordenado pelo Prof. Dr. Willian Fernando Zambuzzi vem atuando no desenvolvimento de ferramentas que possam contornar esta problemática. O objetivo desta monografia é avaliar diferentes scores de bioinformática computados sob um experimento (disponível em base de dados aberta) de sequenciamento de transcriptoma de células cultivadas sobre 21 diferentes biomateriais ósseos. Estes diferentes scores serão então utilizados para a realização de agrupamentos (clusterização) dos biomateriais e ao final disponibilizaremos estes resultados na forma de um aplicativo (ou dashboard) para que usuários possam comparar seus resultados com a base de dados gerada. A este aplicativo daremos o nome de OsteoCLUST. Pretendemos que o produto gerado neste trabalho sirva de base para a construção de um aplicativo mais robusto, desenvolvido futuramente, com potencialidade de se tornar um parâmetro de referência para novas análises, focando sua difusão no setor produtivo na área de biomateriais.
Bone injuries are one of the most common problems to be considered in dentistry and medicine. The reconstruction of lost tissues is one of the great challenges faced by professionals in these areas, often requiring the application of biomaterials capable of good tissue regeneration. Despite the clinical, social and economic relevance of such biomaterials, little progress has been made in bioanalysis methodologies capable of ranking new biomaterials. In the last 10 years, LaBIO, coordinated by Prof. Dr. Willian Fernando Zambuzzi has been working to develop tools that can circumvent this problem. The objective of this monograph is to evaluate different bioinformatics scores computed under an experiment (available in an open database) of transcriptome sequencing of cells grown on 21 different bone biomaterials. These different scores will then be used to perform grouping (clustering) of biomaterials and at the end we will make these results available in the form of an application (or dashboard) so that users can compare their results with the generated database. This application will be called OsteoCLUST. We intend that the product generated in this work will serve as a basis for the construction of a more robust application, developed in the future, with the potential to become a reference parameter for new analyses, focusing on its diffusion in the productive sector in the biomaterials area.
Bone injuries are one of the most common problems to be considered in dentistry and medicine. The reconstruction of lost tissues is one of the great challenges faced by professionals in these areas, often requiring the application of biomaterials capable of good tissue regeneration. Despite the clinical, social and economic relevance of such biomaterials, little progress has been made in bioanalysis methodologies capable of ranking new biomaterials. In the last 10 years, LaBIO, coordinated by Prof. Dr. Willian Fernando Zambuzzi has been working to develop tools that can circumvent this problem. The objective of this monograph is to evaluate different bioinformatics scores computed under an experiment (available in an open database) of transcriptome sequencing of cells grown on 21 different bone biomaterials. These different scores will then be used to perform grouping (clustering) of biomaterials and at the end we will make these results available in the form of an application (or dashboard) so that users can compare their results with the generated database. This application will be called OsteoCLUST. We intend that the product generated in this work will serve as a basis for the construction of a more robust application, developed in the future, with the potential to become a reference parameter for new analyses, focusing on its diffusion in the productive sector in the biomaterials area.
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Palavras-chave
Biomateriais, Bioinformática, Transcriptoma, Aprendizado de máquina, Tecido ósseo