Application of feature selection methods for automated clustering analysis: a review on synthetic datasets.

The effective modelling of high-dimensional data with hundreds to thousands of features remains a challenging task in the field of machine learning. This process is a manually intensive task and requires skilled data scientists to apply exploratory data analysis techniques and statistical methods in pre-processing datasets for meaningful analysis with machine learning methods. However, the massive growth of data has brought about the need for fully automated data analysis methods. One of the key challenges is the accurate selection of a set of relevant features, which can be buried in high-dimensional data along with irrelevant noisy features, by choosing a subset of the complete set of input features that predicts the output with higher accuracy comparable to the performance of the complete input set. Kohonen's self-organising neural network map has been utilised in various ways for this task, such as with the weighted self-organising map (WSOM) approach and this method is reviewed for its efficacy. The study demonstrates that the WSOM approach can result in different results on different runs on a given dataset due to the inappropriate use of the steepest descent optimisation method to minimise the weighted SOM's cost function. An alternative feature weighting approach based on analysis of the SOM after training is presented; the proposed approach allows the SOM to converge before analysing the input relevance, unlike the WSOM that aims to apply weighting to the inputs during the training which distorts the SOM's cost function, resulting in multiple local minimums meaning the SOM does not consistently converge to the same state. We demonstrate the superiority of the proposed method over the WSOM and a standard SOM in feature selection with improved clustering analysis.

Differential activity by polymorphic variants of a remote enhancer that supports galanin expression in the hypothalamus and amygdala: implications for obesity, depression and alcoholism.

The expression of the galanin gene (GAL) in the paraventricular nucleus (PVN) and in the amygdala of higher vertebrates suggests the requirement for highly conserved, but unidentified, regulatory sequences that are critical to allow the galanin gene to control alcohol and fat intake and modulate mood. We used comparative genomics to identify a highly conserved sequence that lay 42 kb 5' of the human GAL transcriptional start site that we called GAL5.1. GAL5.1 activated promoter activity in neurones of the PVN, arcuate nucleus and amygdala that also expressed the galanin peptide. Analysis in neuroblastoma cells demonstrated that GAL5.1 acted as an enhancer of promoter activity after PKC activation. GAL5.1 contained two polymorphisms; rs2513280(C/G) and rs2513281(A/G), that occurred in two allelic combinations (GG or CA) where the dominant GG alelle occurred in 70-83 % of the human population. Intriguingly, both SNPs were found to be in LD (R(2) of 0.687) with another SNP (rs2156464) previously associated with major depressive disorder (MDD). Recreation of these alleles in reporter constructs and subsequent magnetofection into primary rat hypothalamic neurones showed that the CA allele was 40 % less active than the GG allele. This is consistent with the hypothesis that the weaker allele may affect food and alcohol preference. The linkage of the SNPs analysed in this study with a SNP previously associated with MDD together with the functioning of GAL5.1 as a PVN and amygdala specific enhancer represent a significant advance in our ability to understand alcoholism, obesity and major depressive disorder.

Evidence of uneven selective pressure on different subsets of the conserved human genome; implications for the significance of intronic and intergenic DNA.

BACKGROUND: Human genetic variation produces the wide range of phenotypic differences that make us individual. However, little is known about the distribution of variation in the most conserved functional regions of the human genome. We examined whether different subsets of the conserved human genome have been subjected to similar levels of selective constraint within the human population. We used set theory and high performance computing to carry out an analysis of the density of Single Nucleotide Polymorphisms (SNPs) within the evolutionary conserved human genome, at three different selective stringencies, intersected with exonic, intronic and intergenic coordinates. RESULTS: We demonstrate that SNP density across the genome is significantly reduced in conserved human sequences. Unexpectedly, we further demonstrate that, despite being conserved to the same degree, SNP density differs significantly between conserved subsets. Thus, both the conserved exonic and intronic genomes contain a significantly reduced density of SNPs compared to the conserved intergenic component. Furthermore the intronic and exonic subsets contain almost identical densities of SNPs indicating that they have been constrained to the same degree. CONCLUSION: Our findings suggest the presence of a selective linkage between the exonic and intronic subsets and ascribes increased significance to the role of introns in human health. In addition, the identification of increased plasticity within the conserved intergenic subset suggests an important role for this subset in the adaptation and diversification of the human population.