Microarray Gene Expression Data Classification via Wilcoxon Sign Rank Sum and Novel Grey Wolf Optimized Ensemble Learning Models.

Cancer is a deadly disease that affects the lives of people all over the world. Finding a few genes relevant to a single cancer disease can lead to effective treatments. The difficulty with microarray datasets is their high dimensionality; they have a large number of features in comparison to the small number of samples in these datasets. Additionally, microarray data typically exhibit significant asymmetry in dimensionality as well as high levels of redundancy and noise. It is widely held that the majority of genes lack informative value about the classes under study. Recent research has attempted to reduce this high dimensionality by employing various feature selection techniques. This paper presents new ensemble feature selection techniques via the Wilcoxon Sign Rank Sum test (WCSRS) and the Fisher's test (F-test). In the first phase of the experiment, data preprocessing was performed; subsequently, feature selection was performed via the WCSRS and F-test in such a way that the (probability values) p-values of the WCRSR and F-test were adopted for cancerous gene identification. The extracted gene set was used to classify cancer patients using ensemble learning models (ELM), random forest (RF), extreme gradient boosting (Xgboost), cat boost, and Adaboost. To boost the performance of the ELM, we optimized the parameters of all the ELMs using the Grey Wolf optimizer (GWO). The experimental analysis was performed on colon cancer, which included 2000 genes from 62 patients (40 malignant and 22 benign). Using a WCSRS test for feature selection, the optimized Xgboost demonstrated 100% accuracy. The optimized cat boost, on the other hand, demonstrated 100% accuracy using the F-test for feature selection. This represents a 15% improvement over previously reported values in the literature.

Aerobic microbe community and necrophagous insects associated with decomposition of pig carrion poisoned with lead.

This study investigated the effects of pig feed contaminated with lead (Pb) on the decomposition rate of pig carrion, identified the aerobic microorganisms and necrophagous insects associated with decomposing pig (Sus scrofa) carrion (above ground), and explored their potential use in the determination of post-mortem interval (PMI). The study profiled the decomposition of Sus scrofa carrion over a period of 40 days to record the effects of lead during decomposition. Fungi were identified by sub-culturing on prepared solidified potato dextrose agar and microbial identification was carried out using biochemical characterization. The decomposition rate of pigs fed with lead-contaminated feed (0.18 and 0.2 ppm) attracted insects and increased the rate of hair fall, hence at day 35, these carrion were skeletonized. The aerobic bacterial communities identified were Staphylococcus aureus, Bacillus sp. and Salmonella paratyphiwhereas the fungi identified include Fusarium sp., Cylindrocladium sp Cephalosporium sp., Scopolariopsis sp., Aspergillus sp, Mucor sp., Circinella sp., Pythium sp., Penicillium sp., Trichoderma sp., Geotrichum sp., Phytophthora sp., and Saccharomyces sp. The necrophagous insects collected consisted of three orders: Coleoptera, Diptera, and Hymenoptera which included insects like Chrysomya chloropyga Wiedemann, 1818, Musca domesticaLinnaeus, 1758, Sarcophaga exuberansPandelle, 1896, Necrobia rufipesDe Geer, 1775, Dermestes maculatusDe Geer, 1774, Camponotus sericeusFabricius, 1798 and Camponotus perrisiiForel, 1886. The activity of insect on treatments was well matched but the decomposition rate differs. Spectrophotometric analysis of insect larvae collected from decomposing pigs revealed they had presence of lead. Insect larvae and microbes identified are good entomotoxicological tools in crimes associates with lead poisoning.