HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users

Solvent effects on the conformational equilibrium parameters of heterocyclic compounds

The dependences of the thermodynamic parameters of equilibria (Gibbs energies ΔG and enthalpies ΔH) on the dielectric properties of solvents (carbon disulfide, toluene, chloroform, methylene chloride, acetone, and acetonitrile) were analyzed for six types of heterocyclic systems, for which various types of conformational equilibria were observed. The obtained relations between the slopes of the linear dependences of the enthalpies and Gibbs energies of conformational equilibria on medium polarity and the difference of the squares of the dipole moments of the conformers showed that the model of dipole-dipole conformer-medium interactions was incomplete

Solvent effects on the conformational equilibrium parameters of heterocyclic compounds

The dependences of the thermodynamic parameters of equilibria (Gibbs energies ΔG and enthalpies ΔH) on the dielectric properties of solvents (carbon disulfide, toluene, chloroform, methylene chloride, acetone, and acetonitrile) were analyzed for six types of heterocyclic systems, for which various types of conformational equilibria were observed. The obtained relations between the slopes of the linear dependences of the enthalpies and Gibbs energies of conformational equilibria on medium polarity and the difference of the squares of the dipole moments of the conformers showed that the model of dipole-dipole conformer-medium interactions was incomplete

Solvent effects on the conformational equilibrium parameters of heterocyclic compounds

The dependences of the thermodynamic parameters of equilibria (Gibbs energies ΔG and enthalpies ΔH) on the dielectric properties of solvents (carbon disulfide, toluene, chloroform, methylene chloride, acetone, and acetonitrile) were analyzed for six types of heterocyclic systems, for which various types of conformational equilibria were observed. The obtained relations between the slopes of the linear dependences of the enthalpies and Gibbs energies of conformational equilibria on medium polarity and the difference of the squares of the dipole moments of the conformers showed that the model of dipole-dipole conformer-medium interactions was incomplete

A Comparative Machine Learning Modelling Approach for Patients' Mortality Prediction in Hospital Intensive Care Unit

Mortality prediction in a hospital Intensive Care Unit (ICU) is a challenge that must be addressed with high precision. Machine Learning (ML) is a powerful tool in predictive modelling but subject to the problem of class im-balance. In this study, we tackle class imbalance with combining new features, data re-sampling, ensemble learning and an appropriate selection of evaluation metrics in a clinical setting. We built and evaluated 126 ML mod-els to predict mortality in 48546 ICU admissions extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) repository. In our study design, six mortality prediction datasets are extracted; five of which are legacy dataset sets while the remainder is our new constructed dataset. For our combined data models, when testing on isolated data, our selection of features enhanced the prediction performances beyond those for the traditional legacy sets used in research. The legacy datasets are the Simplified Acute Physiology Score (SAPS II), the Sequential Organ Failure Assessment score (SOFA), the Glasgow Coma Scale (GCS), Elixhauser Comorbidity Index (ECI) and Demographics & Disease Groups (DDG). Our approach has a considerable impact on the classification; it resulted in an improvement in the mortality status prediction. For evaluation, we implement a comparative multi-stage evaluation filter for binary classification to compare all models. The best models are identified. The Area Under Receiver Operator Characteristic curves of the tested models range from 0.57 to 0.94. These encouraging results can guide further development of models to allow for more reliable ICU mortality predictions

doi:10.1093/nar/gkn810 HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB