Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔGFs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔGFs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

The Power Of Annual Cost-Of-Illness In Predicting COPD Survival At 3 Years

Clinical signs and lung function are variably sensitive in predicting survival in COPD. 1) Specific and appropriate lung function indices contribute to predicting mortality in COPD effectively. 2) Total annual cost confirms the most sensitive predictor of mortality at three years. 3) Present data support the high predictive power of the careful functional and economic phenotyping in COPD.

Predictive Power in Behavioral Welfare Economics

When choices are inconsistent due to behavioral biases, there is a theoretical debate about whether the structure of a model is necessary for providing precise welfare guidance based on those choices. To address this question empirically, we use standard data sets from the lab and field to evaluate the predictive power of two “model-free” approaches to behavioral welfare analysis. We find they typically have high predictive power, which means there is little ambiguity about what should be selected from each choice set. We also identify properties of revealed preferences that help to explain the predictive power of these approaches.

The identification of a potential prognostic model of colon cancer using integrated bioinformatics analysis

Background This study aimed to construct prognostic model by screening prognostic gene signature of colon cancer. Methods The gene expression profile data of colon cancer were obtained from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) and differently expressed genes (DEGs) between tumor and control samples were identified. Prognosis-associated genes were then identified and used for the construction of prognostic model. The independent factors that associated with the prognosis of colon in the TCGA cohort was identified. Results Totally, 1153 consistent DEGs were screened out between tumor and normal tissues in the TCGA cohort, GSE44861 and GSE44076 datasets. Among these genes, 12 DEGs were related to the prognosis of colon cancer and were used for constructing the prognostic model. This model presented a high predictive power for the prognosis of colon cancer both in the training dataset and in the validation datasets (AUC > 0.8). Statistical analysis showed that age, pathological T, tumor recurrence, and model status were the independent factors for prognosis of patients with colon cancer in TCGA. Conclusions The 12-gene signature prognostic model had a high predictive power for colon cancer prognosis.

Predicting Liver Disease Risk Using a Combination of Common Clinical Markers: A Screening Model from Routine Health Check-Up

Background. Early detection is crucial for the prognosis of patients with autoimmune liver disease (AILD). Due to the relatively low incidence, developing screening tools for AILD remain a challenge. Aims. To analyze clinical characteristics of AILD patients at initial presentation and identify clinical markers, which could be useful for disease screening and early detection. Methods. We performed observational retrospective study and analyzed 581 AILD patients who were hospitalized in the gastroenterology department and 1000 healthy controls who were collected from health management center. Baseline characteristics at initial presentation were used to build regression models. The model was validated on an independent cohort of 56 patients with AILD and 100 patients with other liver disorders. Results. Asymptomatic AILD individuals identified by the health check-up are increased yearly (from 31.6% to 68.0%, p<0.001). The cirrhotic rates at an initial presentation are decreased in the past 18 years (from 52.6% to 20.0%, p<0.001). Eight indicators, which are common in the health check-up, are independent risk factors of AILD. Among them, abdominal lymph node enlargement (LN) positive is the most significant different (OR 8.85, 95% CI 2.73-28.69, p<0.001). The combination of these indicators shows high predictive power (AUC=0.98, sensitivity 89.0% and specificity 96.4%) for disease screening. Except two liver or cholangetic injury makers, the combination of AGE, GENDER, GLB, LN, concomitant extrahepatic autoimmune diseases, and familial history also shows a high predictive power for AILD in other liver disorders (AUC=0.91). Conclusion. Screening for AILD with described parameters can detect AILD in routine health check-up early, effectively and economically. Eight variables in routine health check-up are associated with AILD and the combination of them shows good ability of identifying high-risk individuals.

A computationally-derived model for the solvatochromism of p-phenolates with high predictive power

We report that the positive, reverse or negative solvatochromism of p-phenolate-based dyes is highly correlated with the multireferential (MR) character of their ground-state wave function, with negative compounds presenting the highest degeneracy.

Phytoplankton growth rate modelling: can spectroscopic cell chemotyping be superior to physiological predictors?

Climate change has a strong impact on phytoplankton communities and water quality. However, the development of robust techniques to assess phytoplankton growth is still in progress. In this study, the growth rate of phytoplankton cells grown at different temperatures was modelled based on conventional physiological traits (e.g. chlorophyll, carbon and photosynthetic parameters) using the partial least square regression (PLSR) algorithm and compared with a new approach combining Fourier transform infrared-spectroscopy and PLSR. In this second model, it is assumed that the macromolecular composition of phytoplankton cells represents an intracellular marker for growth. The models have comparable high predictive power ( R 2 > 0.8) and low error in predicting new observations. Interestingly, not all of the predictors present the same weight in the modelling of growth rate. A set of specific parameters, such as non-photochemical fluorescence quenching (NPQ) and the quantum yield of carbon production in the first model, and lipid, protein and carbohydrate contents for the second one, strongly covary with cell growth rate regardless of the taxonomic position of the phytoplankton species investigated. This reflects a set of specific physiological adjustments covarying with growth rate, conserved among taxonomically distant algal species that might be used as guidelines for the improvement of modern primary production models. The high predictive power of both sets of cellular traits for growth rate is of great importance for applied phycological studies. Our approach may find application as a quality control tool for the monitoring of phytoplankton populations in natural communities or in photobioreactors.

Statistical method for estimation of the predictive power of a gene circuit model

In this paper, a specific aspect of the prediction problem is considered: high predictive power is understood as a possibility to reproduce correct behavior of model solutions at predefined values of a subset of parameters. The problem is discussed in the context of a specific mathematical model, the gene circuit model for segmentation gap gene system in early Drosophila development. A shortcoming of the model is that it cannot be used for predicting the system behavior in mutants when fitted to wild type (WT) data. In order to answer a question whether experimental data contain enough information for the correct prediction we introduce two measures of predictive power. The first measure reveals the biologically substantiated low sensitivity of the model to parameters that are responsible for correct reconstruction of expression patterns in mutants, while the second one takes into account their correlation with the other parameters. It is demonstrated that the model solution, obtained by fitting to gene expression data in WT and Kr - mutants simultaneously, and exhibiting the high predictive power, is characterized by much higher values of both measures than those fitted to WT data alone. This result leads us to the conclusion that information contained in WT data is insufficient to reliably estimate the large number of model parameters and provide predictions of mutants.