Identification of significant genes in genomics using Bayesian variable selection methods

Machine learning and systems genomics approaches for multi-omics data

In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

Bayesian Variable Selection under the Proportional Hazards Mixed-effects Model

Over the past decade much statistical research has been carried out to develop models for correlated survival data; however, methods for model selection are still very limited. A stochastic search variable selection (SSVS) approach under the proportional hazards mixed-effects model (PHMM) is developed. The SSVS method has previously been applied to linear and generalized linear mixed models, and to the proportional hazards model with high dimensional data. Because the method has mainly been developed for hierarchical normal mixture distributions, it operates on the linear predictor under the Cox type models. The PHMM naturally incorporates the normal distribution via the random effects, which enables SSVS to efficiently search through the candidate variable space. The approach was evaluated through simulation, and applied to a multi-center lung cancer clinical trial data set, for which the variable selection problem was previously debated upon in the literature.

Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

BACKGROUND

In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).

METHODS

We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method.

RESULTS

The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs.

CONCLUSION

We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.

Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders

Major depressive disorder (MDD) is one of the most common mental disorders worldwide. Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. A common SNP in the brain-derived neurotrophic factor (BDNF) gene, a methionine (Met) substitution for valine (Val) at codon 66 (Val66Met), is a candidate SNP for influencing antidepressant treatment outcome. In this study, our goal was to determine the relationship between the Val66Met polymorphism in the BDNF gene and the rapid antidepressant response to venlafaxine in a Taiwanese population with MDD. Overall, the BDNF Val66Met polymorphism was found not to be associated with short-term venlafaxine treatment outcome. However, the BDNF Val66Met polymorphism showed a trend to be associated with rapid venlafaxine treatment response in female patients. Future research with independent replication in large sample sizes is needed to confirm the role of the BDNF Val66Met polymorphism identified in this study.

A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy

BACKGROUND

Interferon-alpha (IFNalpha) in combination with ribavirin can be used for the treatment of patients with chronic hepatitis C. This therapeutic approach achieves an overall sustained response rate of approximately 40%, but treatment takes 6-12 months and patients often experience significant adverse reactions.

OBJECTIVE

We aim to develop a tool to distinguish potential responders from nonresponders prior to initiation of IFNalpha-ribavirin treatment.

METHODS

Using single nucleotide polymorphisms (SNPs) and viral genotype, we applied the support vector machine (SVM) algorithm to build a tool to predict responsiveness to IFNalpha-ribavirin combination therapy. Furthermore, we utilized the SVM algorithm with the recursive feature elimination method to identify a subset of factors that are significantly more influential than the others.

RESULTS AND CONCLUSION

The SVM model is a promising method for inferring responsiveness to IFNalpha dealing with the complex nonlinear relationship between factors (such as SNPs and viral genotype) and successful therapy. In this study, we demonstrate that our tool may allow patients and doctors to make more informed decisions by analyzing host SNP and viral genotype information.