TAUTOMERS OF CYTOSINE AND THEIR EXCITED ELECTRONIC STATES: A MATRIX ISOLATION SPECTROSCOPIC AND QUANTUM CHEMICAL STUDY

Author Institution: Laboratory of Molecular Spectroscopy, Institute of Chemistry, Eotvos Lorand University, Pf. 32, Budapest, H-1518, Hungary; Laboratory of Theoretical Chemistry, Institute of Chemistry, Eotvos Lorand University, Pf. 32, Budapest, H-1518, HungaryWe have measured the IR and UV spectra of cytosine in a low-temperature argon matrix. An attempt was made to determine the tautomeric ratios existing in the matrix, making use of the matrix-isolation IR spectrum and computed IR intensities of the tautomers in a least squares fitting procedure. The mole fractions are about 0.22 for oxo(-amino) form, 0.26 and 0.44 for the two rotamers, respectively, of the hydroxy(-amino) form and 0.08 for the (oxo-)imino tautomer. These ratios were then used to simulate the matrix-isolation UV spectrum as a composite of the individual spectra, the latter calculated \emph{ab initio} at high levels of electron correlation theory. The agreement between simulated and experimental UV spectra seems satisfactory. This indicates that, in contrast to the solid state and solution spectra described up to now by the oxo(-amino) form alone, the reproduction of the matrix-isolation UV spectrum needs at least the hydroxy(-amino) and oxo(-amino) forms, and probably also the (oxo-)imino form

Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment

BACKGROUND: The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods. METHODS: The aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment. To optimize the model, different feature selection methods were considered. Robust extra-sample error estimation and different loss functions were used to assess model performance. The results were compared with widely used rule-based genotypic interpretation systems (Stanford HIVdb, Rega and ANRS). RESULTS: A set of 3,143 treatment change episodes were extracted from the EuResist database. The dataset included patient demographics, treatment history and viral genotypes. A logistic regression model using high order interaction variables performed better than rule-based genotypic interpretation systems (accuracy 75.63% versus 71.74-73.89%, area under the receiver operating characteristic curve [AUC] 0.76 versus 0.68-0.70) and was equivalent to a random forest model (accuracy 76.16%, AUC 0.77). However, when rule-based genotypic interpretation systems were coupled with additional patient attributes, and the combination was provided as input to the logistic regression model, the performance increased significantly, becoming comparable to the fully data-driven methods. CONCLUSIONS: Patient-derived supplementary features significantly improved the accuracy of the prediction of response to treatment, both with rule-based and data-driven interpretation systems. Fully data-driven models derived from large-scale data sources show promise as antiretroviral treatment decision support tools.status: publishe