Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Review of feature selection techniques in Parkinson's disease using OCT-imaging data

Several spectral-domain optical coherence tomography studies (OCT) reported a decrease on the macular region of the retina in Parkinson’s disease. Yet, the implication of retinal thinning with visual disability is still unclear. Macular scans acquired from patients with Parkinson’s disease (n = 100) and a control group (n = 248) were used to train several supervised classification models. The goal was to determine the most relevant retinal layers and regions for diagnosis, for which univari- ate and multivariate filter and wrapper feature selection methods were used. In addition, we evaluated the classification ability of the patient group to assess the applicability of OCT measurements as a biomarker of the disease

Review of feature selection techniques in Parkinson's disease using OCT-imaging data

Several spectral-domain optical coherence tomography studies (OCT) reported a decrease on the macular region of the retina in Parkinson’s disease. Yet, the implication of retinal thinning with visual disability is still unclear. Macular scans acquired from patients with Parkinson’s disease (n = 100) and a control group (n = 248) were used to train several supervised classification models. The goal was to determine the most relevant retinal layers and regions for diagnosis, for which univari- ate and multivariate filter and wrapper feature selection methods were used. In addition, we evaluated the classification ability of the patient group to assess the applicability of OCT measurements as a biomarker of the disease

Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.The study was partially funded by the “Accion Transversal del Cancer”, approved on the Spanish Ministry Council on the 11th October 2007, by the Instituto de Salud Carlos III-FEDER (PI08/1770, PI08/0533, PI08/1359, PS09/00773, PS09/01286, PS09/01903, PS09/02078, PS09/01662, PI11/01403, PI11/01889, PI11/00226, PI11/01810, PI11/02213, PI12/00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150), by the Fundación Marqués de Valdecilla (API 10/09), by the ICGC International Cancer Genome Consortium CLL, by the Junta de Castilla y León (LE22A10-2), by the Consejería de Salud of the Junta de Andalucía (PI-0571), by the Conselleria de Sanitat of the Generalitat Valenciana (AP 061/10), by the Recercaixa (2010ACUP 00310), by the Regional Government of the Basque Country by European Commission grants FOOD-CT- 2006-036224- HIWATE, by the Spanish Association Against Cancer (AECC) Scientific Foundation, by the The Catalan Government DURSI grant 2009SGR1489. Samples: Biological samples were stored at the Parc de Salut MAR Biobank (MARBiobanc; Barcelona) which is supported by Instituto de Salud Carlos III FEDER (RD09/0076/00036). Furthermore, at the Public Health Laboratory from Gipuzkoa and the Basque Biobank. Furthermore, sample collection was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). Biological samples were stored at the “Biobanco La Fe” which is supported by Instituto de Salud Carlos III (RD 09 0076/00021) and FISABIO biobanking, which is supported by Instituto de Salud Carlos III (RD09 0076/00058).S

Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity

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Exploring the Concept of the Digital Educator During COVID-19

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which have been applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods C4.5 and LASSO. The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicabl

Comparison of Filter Techniques for Two-Step Feature Selection

In the last decade, the processing of the high dimensional data became inevitable task in many areas of research and daily life. Feature selection (FS), as part of the data processing methodology, is an important step in knowledge discovery. This paper proposes nine variation of two-step feature selection approach with filter FS employed in the first step and exhaustive search in the second step. The performance of the proposed methods is comparatively analysed from the stability and predictive performance point of view. As the obtained results indicate the choice of the filter FS in the first stage has strong influence on the resulting stability. Here, the choice of univariate Pearson correlation coefficient based FS method appears to provide the most stable results