Research articles on Melanoma Cancer
Melanoma is a highly immunogenic tumour. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing the treatment of patients in the advanced stage of the disease. In this sense, the Khaos Research Group has worked on multiple occasions in collaboration with physicians to address the problem of melanoma cancer through various algorithmic strategies and the development of tools to support the clinical expert.
This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy.
Paper Link: https://peerj.com/preprints/3260v2/
VIGLA-M is a visual analysis tool for gene expression levels in melanoma patients. As part of this project, we created a database to gather important clinical data and store patient gene expression levels obtained from the NanoString platform. The database is utilized for analyzing the various expression profiles of the patients.
Paper Link: https://doi.org/10.1186/s12859-019-2695-7
GIT Code: https://github.com/KhaosResearch/Vigla-M
In this study, we empirically assess the behaviour of a set of multi-objective particle swarm optimisers based on different archiving and leader selection strategies in the scope of the inference of GRNs. The main goal is to provide system biologists with experimental evidence about which optimisation technique performs with higher success for the inference of consistent GRNs.
Paper Link: https://link.springer.com/article/10.1007/s10489-020-01891-1
FIMED ia a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials. FIMED provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data. This tool is available at https://khaos.uma.es/fimedV2/
Paper Link: https://doi.org/10.1016/j.cmpb.2021.106496
Git Code: https://github.com/sandrohr95/FIMED
This paper represents a first attempt to experimentally and technically investigate the explainability of modern XAI methods Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), in terms of reproducibility of results and execution time on a Melanoma image classification data set. This paper shows that XAI methods provide advantages on model result interpretation in Melanoma image classification.
Paper Link: https://doi.org/10.1007/978-3-031-07704-3_26
This paper proposes Ensemble-based Genetic Algorithm Explainer (EGAE) for melanoma cancer detection that automatically detects and presents the informative sections of the image to the user. Experimental results on a melanoma dataset show that EGAE automatically detects informative lesions.
Paper Link: https://doi.org/10.1016/j.compbiomed.2023.106613
Git Code: https://github.com/KhaosResearch/EGAE
