(-)-Mandelic acid benzyl ester

27536414

The structures of two facially coordinated Group VII metal complexes, fac-[ReCl(C10H8N2O2)(CO)3]·C4H8O (I·THF) and fac-[MnBr(C10H8N2O2)(CO)3]·C4H8O (II·THF), are reported. In both complexes, the metal ion is coordinated by three carbonyl ligands, a halide ligand, and a 6,6′-dihy­droxy-2,2′-bi­pyridine ligand in a distorted octa­hedral geometry. Both complexes co-crystallize with a non-coordinating tetra­hydro­furan (THF) solvent mol­ecule and exhibit inter­molecular but not intra­molecular hydrogen bonding. In both crystal structures, chains of complexes are formed due to inter­molecular hydrogen bonding between a hy­droxy group from the 6,6′-dihy­droxy-2,2′-bi­pyridine ligand and the halide ligand from a neighboring complex. The THF mol­ecule is hydrogen bonded to the remaining hy­droxy group.

crystal structure, 6,6′-dihy­droxy-2,2′-bi­pyridine ligand, rhenium complex, manganese complex, hydrogen bonding, selective catalysts for CO2 reduction

Crystal structures of fac-tri­carbonyl­chlorido­(6,6′-dihy­droxy-2,2′-bi­pyridine)­rhenium(I) tetra­hydro­furan monosolvate and fac-bromido­tricarbon­yl(6,6′-dihy­droxy-2,2′-bi­pyridine)­manganese(I) tetra­hydro­furan monosolvate

Sheri Lense,a,* Nicholas A. Piro,b Scott W. Kassel,b Andrew Wildish,a and Brent Jefferya

2016 Aug 1

29728051

Background
Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares – Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals’ outlierness based on the Cook’s distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.

Results
We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified.

Conclusions
The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.

Electronic supplementary material
The online version of this article (10.1186/s12859-018-2149-7) contains supplementary material, which is available to authorized users.

Ensemble modeling, High-dimensionality, Outlier detection, Rank Product test, Triple-negative breast cancer

Ensemble outlier detection and gene selection in triple-negative breast cancer data

Marta B. Lopes,1 Andre Verissimo,1 Eunice Carrasquinha,1 Sandra Casimiro,2 Niko Beerenwinkel,3,4 and Susana Vingacorresponding author1,5

2018;