Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Supervised wavelet method to predict patient survival from gene expression data.

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Wavelet-Based Cancer Drug Recommender System

A natureza molecular do cancro serve de base para estudos sistemáticos de genomas cancerígenos, fornecendo valiosos insights e permitindo o desenvolvimento de tratamentos clínicos. Acima de tudo, estes estudos estão a impulsionar o uso clínico de informação genómica na escolha de tratamentos, de outro modo não expectáveis, em pacientes com diversos tipos de cancro, possibilitando a medicina de precisão. Com isso em mente, neste projeto combinamos técnicas de processamento de imagem, para aprimoramento de dados, e sistemas de recomendação para propor um ranking personalizado de drogas anticancerígenas. O sistema é implementado em Python e testado usando uma base de dados que contém registos de sensibilidade a drogas, com mais de 310.000 IC50 que, por sua vez, descrevem a resposta de mais de 300 drogas anticancerígenas em 987 linhas celulares cancerígenas. Após várias tarefas de pré-processamento, são realizadas duas experiências. A primeira experiência usa as imagens originais de microarrays de DNA e a segunda usa as mesmas imagens, mas submetidas a uma transformada wavelet. As experiências confirmam que as imagens de microarrays de DNA submetidas a transformadas wavelet melhoram o desempenho do sistema de recomendação, otimizando a pesquisa de linhas celulares cancerígenas com perfil semelhante ao da nova linha celular. Além disso, concluímos que as imagens de microarrays de DNA com transformadas de wavelet apropriadas, não apenas fornecem informações mais ricas para a pesquisa de utilizadores similares, mas também comprimem essas imagens com eficiência, otimizando os recursos computacionais. Tanto quanto é do nosso conhecimento, este projeto é inovador no que diz respeito ao uso de imagens de microarrays de DNA submetidas a transformadas wavelet, para perfilar linhas celulares num sistema de recomendação personalizado de drogas anticancerígenas

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics