Benchmarking relief-based feature selection methods for bioinformatics data mining

Urbanowicz, Ryan J.; Olson, Randal S.; Schmitt, Peter A.; Meeker, Melissa; Moore, Jason H.

doi:10.1016/j.jbi.2018.07.015

Cited by 196 publications

(211 citation statements)

References 50 publications

(142 reference statements)

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“…Spatially uniform ReliefF (SURF) (Greene, Penrod, Kiralis, & Moore, ) is an extension of ReliefF that, instead of choosing a constant number of nearest neighbors, selects all neighbors within a fixed distance

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of the individual. Furthermore, it has been recommended to apply an iterative Relief approach such as tuned ReliefF (TURF) in large feature space with more than 10,000 features (Urbanowicz et al, ). TURF algorithm (Moore & White, ) improves the performance of ReliefF by running it several times.…”

Section: Methodsmentioning

confidence: 99%

A network approach to prioritizing susceptibility genes for genome‐wide association studies

Kafaie

Chen

2019

Genetic Epidemiology

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The heritability of complex diseases including cancer is often attributed to multiple interacting genetic alterations. Such a non‐linear, non‐additive gene–gene interaction effect, that is, epistasis, renders univariable analysis methods ineffective for genome‐wide association studies. In recent years, network science has seen increasing applications in modeling epistasis to characterize the complex relationships between a large number of genetic variations and the phenotypic outcome. In this study, by constructing a statistical epistasis network of colorectal cancer (CRC), we proposed to use multiple network measures to prioritize genes that influence the disease risk of CRC through synergistic interaction effects. We computed and analyzed several global and local properties of the large CRC epistasis network. We utilized topological properties of network vertices such as the edge strength, vertex centrality, and occurrence at different graphlets to identify genes that may be of potential biological relevance to CRC. We found 512 top‐ranked single‐nucleotide polymorphisms, among which COL22A1, RGS7, WWOX, and CELF2 were the four susceptibility genes prioritized by all described metrics as the most influential on CRC.

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Section: Methodsmentioning

confidence: 99%

A network approach to prioritizing susceptibility genes for genome‐wide association studies

Kafaie

Chen

2019

Genetic Epidemiology

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“…Based on the iid assumption for X ia and X ja , it follows from Thm. 2.1 that the joint density function g (1) of X ia and Z q a is given by…”

Section: Distribution Of |Dmentioning

confidence: 99%

“…Let F Z q a denote the distribution function of the random variable Z q a . Furthermore, we define the events E (1) and E (2) as…”

Section: Distribution Of |Dmentioning

confidence: 99%

“…Similar to the mean (Eq. 38), the limiting variance of D (1) ij (Eq. 39) grows on the order of feature dimension p, which implies that points become more dispersed as the dimension increases.…”

Section: Standard Normal Datamentioning

confidence: 99%

“…For random normal data (N (0, 1)), for example, the variance of the pairwise distances of a Manhattan metric is proportional to the number of attributes (p) whereas the variance is constant for a Euclidean metric. Relief methods [1][2][3] and nearest-neighbor projected distance regression (NDPR) [4] use nearest neighbors to compute attribute importance scores and often use adaptive neighborhoods that rely on the mean and variance of the distance distribution. The ability of this class of methods to identify association effects, like main effects or interaction effects, depends on parameters such as neighborhood radii or number of neighbors k [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Theoretical properties of nearest-neighbor distance distributions and novel metrics for high dimensional bioinformatics data

Dawkins

McKinney

2019

Preprint

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The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. The effects of the distribution and metric, as well as the presence of correlation and interactions, are reflected in the expected moments of the distribution of pairwise distances. We derive general analytical expressions for the mean and variance of pairwise distances for L q metrics for normal and uniform random data with p attributes and m instances. We use extreme value theory to derive results for metrics that are normalized by the range of each attribute (max -min). In addition to these expressions for continuous data, we derive similar analytical formulas for a new metric for genetic variants (categorical data) in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance properties. This metric is applicable to correlation-based predictors derived from time series data. Derivations assume independent data, but empirically we also consider the effect of correlation. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies including gene expression, GWAS, and fMRI data. The summary of distribution moments and detailed derivations provide a resource for understanding the distance properties for various metrics and data types.

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Identification of potential vascular endothelial growth factor receptor inhibitors via tree‐based learning modeling and molecular docking simulation

Arabi,

Torabi,

Fassihi

et al. 2024

Journal of Chemometrics

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Angiogenesis, a crucial process in tumor growth, is widely recognized as a key factor in cancer progression. The vascular endothelial growth factor (VEGF) signaling pathway is important for its pivotal role in promoting angiogenesis. The primary objective of this study was to identify a powerful classifier for distinguishing compounds as active or inactive inhibitors of VEGF receptors. To build the machine learning model, compounds were sourced from the BindingDB database. A variety of common feature selection techniques, including both filter‐based and wrapper‐based methods, were applied to reduce dimensionality, subsequently, overfitting problem. Robust and accurate tree‐based classifiers were employed in the classification procedure. Application of the extra‐tree classifier using the MultiSURF* feature selection method provided a model with superior accuracy (83.7%) compared with other feature selection techniques. High‐throughput molecular docking followed by an accurate docking and comprehensive analysis of the results was performed to provide the best possible inhibitors of these receptors. Comprehensive analysis of the docking results revealed successful prediction of molecules with VEGFR1 and VEGFR2 inhibitory activity. These results emphasized that the performance of the extra‐tree model, coupled with MultiSURF* feature selection, surpassed other methods in identifying chemical compounds targeting specific VEGF receptors.

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Benchmarking relief-based feature selection methods for bioinformatics data mining

Cited by 196 publications

References 50 publications

A network approach to prioritizing susceptibility genes for genome‐wide association studies

A network approach to prioritizing susceptibility genes for genome‐wide association studies

Theoretical properties of nearest-neighbor distance distributions and novel metrics for high dimensional bioinformatics data

Identification of potential vascular endothelial growth factor receptor inhibitors via tree‐based learning modeling and molecular docking simulation

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