How molecular diagnostics based on gene expressions can improve diagnostics in Sarcoma
(2023) EEML05 20231Department of Biomedical Engineering
- Abstract
- Sarcoma, a rare and heterogeneous cancer type, present significant diagnostic challenges due to its numerous subtypes. Molecular diagnostics and machine learning models have emerged as promising tools to enhance sarcoma diagnosis. This study, conducted at Qlucore, aims to explore the usage of these new techniques in improving sarcoma diagnostics, with a specific focus on soft tissue sarcomas.
The primary purpose of the study is to investigate the classification of different subtypes of soft tissue sarcoma based on gene expression analysis. This is performed by investigating and evaluating various classification methods. The study also explores alternative approaches for achieving accurate classification.
The results of this study... (More) - Sarcoma, a rare and heterogeneous cancer type, present significant diagnostic challenges due to its numerous subtypes. Molecular diagnostics and machine learning models have emerged as promising tools to enhance sarcoma diagnosis. This study, conducted at Qlucore, aims to explore the usage of these new techniques in improving sarcoma diagnostics, with a specific focus on soft tissue sarcomas.
The primary purpose of the study is to investigate the classification of different subtypes of soft tissue sarcoma based on gene expression analysis. This is performed by investigating and evaluating various classification methods. The study also explores alternative approaches for achieving accurate classification.
The results of this study demonstrate promising potential for the clinical use of molecular diagnostics in accurately diagnosing specific subtypes of sarcoma. However, certain sarcoma subgroups present challenges in classification. The study suggests the adoption of hierarchical classifiers as a potential solution for this. Furthermore, the study emphasizes that the choice of algorithm significantly impacts classification outcomes. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9129489
- author
- Friberg, Emma LU and Lennartsson, Alma LU
- supervisor
- organization
- alternative title
- Hur molekylär diagnostik baserat på genuttryck kan förbättra diagnostiseringen av Sarkom
- course
- EEML05 20231
- year
- 2023
- type
- M2 - Bachelor Degree
- subject
- language
- English
- id
- 9129489
- date added to LUP
- 2023-06-26 11:32:59
- date last changed
- 2023-06-26 11:32:59
@misc{9129489,
abstract = {{Sarcoma, a rare and heterogeneous cancer type, present significant diagnostic challenges due to its numerous subtypes. Molecular diagnostics and machine learning models have emerged as promising tools to enhance sarcoma diagnosis. This study, conducted at Qlucore, aims to explore the usage of these new techniques in improving sarcoma diagnostics, with a specific focus on soft tissue sarcomas.
The primary purpose of the study is to investigate the classification of different subtypes of soft tissue sarcoma based on gene expression analysis. This is performed by investigating and evaluating various classification methods. The study also explores alternative approaches for achieving accurate classification.
The results of this study demonstrate promising potential for the clinical use of molecular diagnostics in accurately diagnosing specific subtypes of sarcoma. However, certain sarcoma subgroups present challenges in classification. The study suggests the adoption of hierarchical classifiers as a potential solution for this. Furthermore, the study emphasizes that the choice of algorithm significantly impacts classification outcomes.}},
author = {{Friberg, Emma and Lennartsson, Alma}},
language = {{eng}},
note = {{Student Paper}},
title = {{How molecular diagnostics based on gene expressions can improve diagnostics in Sarcoma}},
year = {{2023}},
}