Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Prediction of Ligand Binding Using an Approach Designed to Accommodate Diversity in Protein-Ligand Interactions

Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys (r2, 0.895 for a series of typical proteins). Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics

Abnormal social reward processing in autism as indexed by pupillary responses to happy faces

Background: Individuals with Autism Spectrum Disorders (ASD) typically show impaired eye contact during social interactions. From a young age, they look less at faces than typically developing (TD) children and tend to avoid direct gaze. However, the reason for this behavior remains controversial; ASD children might avoid eye contact because they perceive the eyes as aversive or because they do not find social engagement through mutual gaze rewarding. Methods: We monitored pupillary diameter as a measure of autonomic response in children with ASD (n = 20, mean age = 12.4) and TD controls (n = 18, mean age = 13.7) while they looked at faces displaying different emotions. Each face displayed happy, fearful, angry or neutral emotions with the gaze either directed to or averted from the subjects. Results: Overall, children with ASD and TD controls showed similar pupillary responses; however, they differed significantly in their sensitivity to gaze direction for happy faces. Specifically, pupillary diameter increased among TD children when viewing happy faces with direct gaze as compared to those with averted gaze, whereas children with ASD did not show such sensitivity to gaze direction. We found no group differences in fixation that could explain the differential pupillary responses. There was no effect of gaze direction on pupil diameter for negative affect or neutral faces among either the TD or ASD group. Conclusions: We interpret the increased pupillary diameter to happy faces with direct gaze in TD children to reflect the intrinsic reward value of a smiling face looking directly at an individual. The lack of this effect in children with ASD is consistent with the hypothesis that individuals with ASD may have reduced sensitivity to the reward value of social stimuli

TRIM16 acts as a tumour suppressor by inhibitory effects on cytoplasmic vimentin and nuclear E2F1 in neuroblastoma cells

The family of tripartite-motif (TRIM) proteins are involved in diverse cellular processes, but are often characterized by critical protein–protein interactions necessary for their function. TRIM16 is induced in different cancer types, when the cancer cell is forced to proceed down a differentiation pathway. We have identified TRIM16 as a DNA-binding protein with histone acetylase activity, which is required for the retinoic acid receptor β2 transcriptional response in retinoid-treated cancer cells. In this study, we show that overexpressed TRIM16 reduced neuroblastoma cell growth, enhanced retinoid-induced differentiation and reduced tumourigenicity in vivo. TRIM16 was only expressed in the differentiated ganglion cell component of primary human neuroblastoma tumour tissues. TRIM16 bound directly to cytoplasmic vimentin and nuclear E2F1 in neuroblastoma cells. TRIM16 reduced cell motility and this required downregulation of vimentin. Retinoid treatment and enforced overexpression caused TRIM16 to translocate to the nucleus, and bind to and downregulate nuclear E2F1, required for cell replication. This study, for the first time, demonstrates that TRIM16 acts as a tumour suppressor, affecting neuritic differentiation, cell migration and replication through interactions with cytoplasmic vimentin and nuclear E2F1 in neuroblastoma cells

Neddylation inhibition upregulates PD‐L1 expression and enhances the efficacy of immune checkpoint blockade in glioblastoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149569/1/ijc32379_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149569/2/ijc32379-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149569/3/ijc32379.pd

The VEGF -634G>C promoter polymorphism is associated with risk of gastric cancer

<p>Abstract</p> <p>Background</p> <p>Both TGF-β1 and VEGF play a critic role in the multiple-step process of tumorgenesis of gastric cancer. Single nucleotide polymorphisms (SNPs) of the <it>TGFB1 </it>and <it>VEGF </it>genes have been associated with risk and progression of many cancers. In this study, we investigated the association between potentially functional SNPs of these two genes and risk of gastric cancer in a US population.</p> <p>Methods</p> <p>The risk associated with genotypes and haplotypes of four <it>TGFB1 </it>SNPs and four <it>VEGF </it>SNPs were determined by multivariate logistic regression analysis in 171 patients with gastric cancer and 353 cancer-free controls frequency-matched by age, sex and ethnicity.</p> <p>Results</p> <p>Compared with the <it>VEGF</it>-634GG genotype, the -634CG genotype and the combined -634CG+CC genotypes were associated with a significantly elevated risk of gastric cancer (adjusted OR = 1.88, 95% CI = 1.24-2.86 and adjusted OR = 1.56, 95% CI = 1.07-2.27, respectively). However, none of other <it>TGFB1 </it>and <it>VEGF </it>SNPs was associated with risk of gastric cancer.</p> <p>Conclusion</p> <p>Our data suggested that the <it>VEGF</it>-634G>C SNP may be a marker for susceptibility to gastric cancer, and this finding needs to be validated in larger studies.</p

An Ultra-Fast Metabolite Prediction Algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy

A pilot study to evaluate the effect of Taeumjowi-tang on obesity in Korean adults: study protocol for a randomised, double-blind, placebo-controlled, multicentre trial

<p>Abstract</p> <p>Background</p> <p>Obesity, which is described as excessive or abnormal body fat, increases the risk of diet-related diseases. In Korea and around the world, the prevalence of obesity has grown annually from 1998 to 2008. This growth has continued despite various therapeutic efforts. The discovery of new and alternative treatments for obesity should be considered an important priority. Taeumjowi-tang (TJ001), a traditional Korean medicinal extract consisting of eight herbs, is a widely used herbal remedy for obesity in Korea. However, the efficacy and safety of TJ001 have not been fully investigated in a clinical trial. The purpose of this pilot study is to estimate obesity-related parameters and to assess the efficacy and safety of TJ001.</p> <p>Methods</p> <p>Our study is a randomised, double-blind, placebo-controlled, multicentre clinical trial of Taeumjowi-tang (TJ001). For this study, we will recruit obese Korean patients of both sexes, ages 18 to 65 years, from four university hospitals. A total of 104 subjects will be recruited. The participants will receive either 7 g of TJ001 or a placebo three times daily for 12 weeks. The primary end point will be the rate of subjects who lose at least 5% of their baseline body weight. The secondary end points will be changes in body weight, body mass index, waist circumference, hip circumference, waist/hip circumference ratio, lipid profiles, body fat composition, blood pressure, fasting glucose concentration, C-reactive protein and questionnaires related to the quality of life. The outcomes will be measured every 4 weeks. The study period will be 12 weeks and will include a total of five visits with each subject (at screening and at 0, 4, 8 and 12 weeks).</p> <p>Conclusions</p> <p>The results of our study will inform various estimates of TJ001 and will serve as the basis for a larger-scale trial. This study will assess the efficacy and safety of TJ001 as an alternative herbal remedy for obesity.</p> <p>Trial registration</p> <p>Current Controlled Trials <a href="http://www.controlled-trials.com/ISRCTN87153759">ISRCTN87153759</a></p