Self-Adaptive particle swarm optimization for large-scale feature selection in classification

© 2019 Association for Computing Machinery. Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to fnd a suitable EC method and corresponding suitable parameter values for a given largescale feature selection problem if we want to solve it effectively and efciently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for largescale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classifcation accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems. © Xue 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'ACM Transactions on Knowledge Discovery from Data', https://dx.doi.org/10.1145/3340848

Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S1 - Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells

<p>Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S1 for Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells by Y Xue, J Wang, Y Huang, X Gao, L Kong, T Zhang and M Tang in Human & Experimental Toxicology</p

Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S2 - Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells

<p>Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S2 for Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells by Y Xue, J Wang, Y Huang, X Gao, L Kong, T Zhang and M Tang in Human & Experimental Toxicology</p

A common 1.6 mb Y-chromosomal inversion predisposes to subsequent deletions and severe spermatogenic failure in humans

Male infertility is a prevalent condition, affecting 5–10% of men. So far, few genetic factors have been described as contributors to spermatogenic failure. Here, we report the first re-sequencing study of the Y-chromosomal Azoospermia Factor c (AZFc) region, combined with gene dosage analysis of the multicopy DAZ, BPY2, and CDYgenes and Y-haplogroup determination. In analysing 2324 Estonian men, we uncovered a novel structural variant as a high-penetrance risk factor for male infertility. The Y lineage R1a1-M458, reported at >20% frequency in several European populations, carries a fixed ~1.6 Mb r2/r3 inversion, destabilizing the AZFc region and predisposing to large recurrent microdeletions. Such complex rearrangements were significantly enriched among severe oligozoospermia cases. The carrier vs non-carrier risk for spermatogenic failure was increased 8.6-fold (p=6.0×10−4). This finding contributes to improved molecular diagnostics and clinical management of infertility. Carrier identification at young age will facilitate timely counselling and reproductive decision-making

Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations.

We genotyped 738 individuals belonging to 49 populations from Nepal, Bhutan, North India, or Tibet at over 500,000 SNPs, and analyzed the genotypes in the context of available worldwide population data in order to investigate the demographic history of the region and the genetic adaptations to the harsh environment. The Himalayan populations resembled other South and East Asians, but in addition displayed their own specific ancestral component and showed strong population structure and genetic drift. We also found evidence for multiple admixture events involving Himalayan populations and South/East Asians between 200 and 2,000 years ago. In comparisons with available ancient genomes, the Himalayans, like other East and South Asian populations, showed similar genetic affinity to Eurasian hunter-gatherers (a 24,000-year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya. The high-altitude Himalayan populations all shared a specific ancestral component, suggesting that genetic adaptation to life at high altitude originated only once in this region and subsequently spread. Combining four approaches to identifying specific positively selected loci, we confirmed that the strongest signals of high-altitude adaptation were located near the Endothelial PAS domain-containing protein 1 and Egl-9 Family Hypoxia Inducible Factor 1 loci, and discovered eight additional robust signals of high-altitude adaptation, five of which have strong biological functional links to such adaptation. In conclusion, the demographic history of Himalayan populations is complex, with strong local differentiation, reflecting both genetic and cultural factors; these populations also display evidence of multiple genetic adaptations to high-altitude environments