scholarly journals QSAR of caffeines by similarity cluster prediction

2014 ◽  
Vol 12 (3) ◽  
pp. 365-376 ◽  
Author(s):  
Teodora Harsa ◽  
Alexandra Harsa ◽  
Beata Szefler
Keyword(s):  
Predictive Model ◽  
Log P ◽  
Test Set ◽  
Pubchem Database ◽  
External Test

AbstractA novel QSAR approach based on correlation weighting and alignment over a hypermolecule that mimics the investigated correlational space was performed on a set of 40 caffeines downloaded from the PubChem database. The best models describing log P and LD50 values of this set of caffeine derivatives were validated against the external test set and in a new predictive model by using clusters of similarity.

scholarly journals Fourier transform infrared spectroscopy of milk samples as a tool to estimate energy balance, energy- and dry matter intake in lactating dairy cows

2020 ◽  
pp. 1-8
Author(s):  
Amira Rachah ◽  
Olav Reksen ◽  
Nils Kristian Afseth ◽  
Valeria Tafintseva ◽  
Sabine Ferneborg ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Dry Matter ◽  
External Validation ◽  
Ftir Spectra ◽  
Dry Matter Intake ◽  
Energy Status ◽  
Test Set ◽  
Milk Samples ◽  
External Test ◽  
Body Energy

Abstract The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.

scholarly journals Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial

2021 ◽  
Vol 09 (06) ◽  
pp. E955-E964
Author(s):  
Ganggang Mu ◽  
Yijie Zhu ◽  
Zhanyue Niu ◽  
Shigang Ding ◽  
Honggang Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Atrophic Gastritis ◽  
Intestinal Metaplasia ◽  
Layer By Layer ◽  
Test Set ◽  
White Light Endoscopy ◽  
High Risk Factors ◽  
External Test ◽  
Diagnostic Trial

Abstract Background and study aims Endoscopy plays a crucial role in diagnosis of gastritis. Endoscopists have low accuracy in diagnosing atrophic gastritis with white-light endoscopy (WLE). High-risk factors (such as atrophic gastritis [AG]) for carcinogenesis demand early detection. Deep learning (DL)-based gastritis classification with WLE rarely has been reported. We built a system for improving the accuracy of diagnosis of AG with WLE to assist with this common gastritis diagnosis and help lessen endoscopist fatigue. Methods We collected a total of 8141 endoscopic images of common gastritis, other gastritis, and non-gastritis in 4587 cases and built a DL -based system constructed with UNet + + and Resnet-50. A system was developed to sort common gastritis images layer by layer: The first layer included non-gastritis/common gastritis/other gastritis, the second layer contained AG/non-atrophic gastritis, and the third layer included atrophy/intestinal metaplasia and erosion/hemorrhage. The convolutional neural networks were tested with three separate test sets. Results Rates of accuracy for classifying non-atrophic gastritis/AG, atrophy/intestinal metaplasia, and erosion/hemorrhage were 88.78 %, 87.40 %, and 93.67 % in internal test set, 91.23 %, 85.81 %, and 92.70 % in the external test set ,and 95.00 %, 92.86 %, and 94.74 % in the video set, respectively. The hit ratio with the segmentation model was 99.29 %. The accuracy for detection of non-gastritis/common gastritis/other gastritis was 93.6 %. Conclusions The system had decent specificity and accuracy in classification of gastritis lesions. DL has great potential in WLE gastritis classification for assisting with achieving accurate diagnoses after endoscopic procedures.

scholarly journals Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors

2018 ◽  
Vol 19 (11) ◽  
pp. 3423 ◽  
Author(s):  
Ting Wang ◽  
Lili Tang ◽  
Feng Luan ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Correlation Coefficient ◽  
Binary Mixtures ◽  
Quantitative Structure Activity Relationship ◽  
Training Set ◽  
Statistical Parameters ◽  
Test Set ◽  
Qsar Models ◽  
Forward Stepwise ◽  
Leave One Out ◽  
External Test

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq2 (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R2 = 0.853, qext2 (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R2 = 0.925, LOOq2 = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R2 = 0.896, qext2 = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

In Silico Prediction of Blood Brain Barrier Permeability

2011 ◽  
pp. 155-171 ◽  
Author(s):  
Zhi Wang ◽  
Aixia Yan ◽  
Jiaxuan Li
Keyword(s):  
Blood Brain Barrier ◽  
Brain Barrier ◽  
Support Vector ◽  
Test Set ◽  
Barrier Permeability ◽  
Blood Brain Barrier Permeability ◽  
Blood Brain ◽  
Physicochemical Descriptors ◽  
Brain Barrier Permeability ◽  
External Test

The ability of penetration of the blood-brain barrier is an important property for the development of Central Nervous System drugs, which is commonly expressed by logBB (logBB = log(Cbrain/Cblood). In this work, a support vector machine was used to build quantitative models of blood brain barrier permeability. Molecular descriptors for 182 compounds were calculated by ADRIANA.Code and 12 descriptors were selected using the automatic variable selection function in Weka. Based on two common physicochemical descriptors (xlogP and Topological Polar Surface Area (TPSA)) and 10 2D property autocorrelation descriptors on atom pair properties, an SVM regression model was built. The built model was validated by an external test set. The reliable predictions of the test set demonstrate that this model performs well and can be used for estimation of logBB values for drug and drug-like molecules.

scholarly journals Prediction of Biopharmaceutical Drug Disposition Classification System (BDDCS) by Structural Parameters

2019 ◽  
Vol 22 ◽  
pp. 247-269 ◽  
Author(s):  
Yeganeh Golfar ◽  
Ali Shayanfar
Keyword(s):  
Classification System ◽  
Drug Disposition ◽  
Structural Parameters ◽  
Log P ◽  
Metabolizing Enzymes ◽  
Acceptable Accuracy ◽  
Water Partition Coefficient ◽  
Pharmacokinetic Properties ◽  
Solvation Parameters ◽  
External Test

Modeling of physicochemical and pharmacokinetic properties is important for the prediction and mechanism characterization in drug discovery and development. Biopharmaceutics Drug Disposition Classification System (BDDCS) is a four-class system based on solubility and metabolism. This system is employed to delineate the role of transporters in pharmacokinetics and their interaction with metabolizing enzymes. It further anticipates drug disposition and potential drug-drug interactions in the liver and intestine. According to BDDCS, drugs are classified into four groups in terms of the extent of metabolism and solubility (high and low). In this study, structural parameters of drugs were used to develop classification-based models for the prediction of BDDCS class. Reported BDDCS data of drugs were collected from the literature, and structural descriptors (Abraham solvation parameters and octanol–water partition coefficient (log P)) were calculated by ACD/Labs software. Data were divided into training and test sets. Classification-based models were then used to predict the class of each drug in BDDCS system using structural parameters and the validity of the established models was evaluated by an external test set. The results of this study showed that log P and Abraham solvation parameters are able to predict the class of solubility and metabolism in BDDCS system with good accuracy. Based on the developed methods for prediction solubility and metabolism class, BDDCS could be predicted in the correct with an acceptable accuracy. Structural properties of drugs, i.e. logP and Abraham solvation parameters (polarizability, hydrogen bonding acidity and basicity), are capable of estimating the class of solubility and metabolism with an acceptable accuracy.

Fraction Lipophilicity Index (FLI)

2019 ◽  
Vol 4 (1) ◽  
pp. 41-66
Author(s):  
Anna Tsantili-Kakoulidou ◽  
Maria Chatzopoulou ◽  
Vassilis J. Demopoulos
Keyword(s):  
Oral Drug ◽  
Log P ◽  
Oral Drugs ◽  
Test Set ◽  
Weighted Combination

Fraction lipophilicity index (FLI) was developed as a metric for assessing oral drug likeness of ionizable chemical entities, as a weighted combination of log P and log D according to equation: FLI = 2xlog P–log D. The dataset included basic and acidic oral drugs introduced worldwide from 1994-2013. Using MedChemDesigner for logP and logD calculations, a drug-like FLI range of 1-8 is defined, whereas ClogP leads to a broader FLI(C) range of 1-10. A comparison of FLI with Rule of 5 showed that oral drugs with a two fold violation were well accommodated within the specified FLI ranges. Calculations of FLI and FLI(C) for 41 drugs with poor/moderately absorption showed that 40% of them have values outside the suggested drug-like ranges, while a distinct gap in the FLI and FLI(C) space permits the recommendation of ‘safer' ranges: for bases between 5-8 and 5-10 for FLI and FLI(C), respectively, and for acids between 4-7. Application of FLI to a test set of investigational compounds placed all of them within the drug-like FLI/ FLI(C) range, while discriminating two out of three low permeable molecules.

TOPOCHEMICAL MODELS FOR THE PREDICTION OF LIPOPHILICITY OF 1,3-DISUBSTITUTED PROPAN-2-ONE ANALOGS

2007 ◽  
Vol 06 (03) ◽  
pp. 435-448 ◽  
Author(s):  
HARISH DUREJA ◽  
VIPIN KUMAR ◽  
SUNIL GUPTA ◽  
ANIL KUMAR MADAN
Keyword(s):  
Log P ◽  
Molecular Connectivity ◽  
Data Set ◽  
Test Set ◽  
P Values ◽  
Independent Test ◽  
Wiener’S Topochemical Index ◽  
Leave One Out ◽  
Eccentric Connectivity Topochemical Index ◽  
Molecular Connectivity Topochemical Index

In the present study, the relationship between the topochemical indices and log P values of 1,3-disubstituted propan-2-one analogs has been investigated. Three topochemical indices, Wiener's topochemical index — a distance-based topochemical descriptor, molecular connectivity topochemical index — an adjacency-based topochemical descriptor, and eccentric connectivity topochemical index — an adjacency-cum-distance-based topochemical descriptor, were used for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index were computed for each of the 45 analogs constituting the data set using an in-house computer program. The predicted log P values using leave-one-out (LOO) procedure exhibited a q2 of 0.72, 0.70, and 0.71 with reported log P values for Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index, respectively. Separate models were developed using training set and log P of each analog in the independent test set was predicted using these models. The correlation of predicted log P values with the reported values, for independent test set, were in good agreement with those predicted using LOO procedure.

scholarly journals Segmentation of Cerebral Small Vessel Diseases-White Matter Hyperintensities Based on a Deep Learning System

2021 ◽  
Vol 8 ◽  
Author(s):  
Wei Shan ◽  
Yunyun Duan ◽  
Yu Zheng ◽  
Zhenzhou Wu ◽  
Shang Wei Chan ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
White Matter Hyperintensities ◽  
Automatic Segmentation ◽  
Learning System ◽  
Test Set ◽  
Minor Revision ◽  
Lesion Level ◽  
External Test ◽  
Cerebral Small Vessel Diseases

Objective: Reliable quantification of white matter hyperintensities (WHMs) resulting from cerebral small vessel diseases (CSVD) is essential for understanding their clinical impact. We aim to develop and clinically validate a deep learning system for automatic segmentation of CSVD-WMH from fluid-attenuated inversion recovery (FLAIR) imaging using large multicenter data.Method: A FLAIR imaging dataset of 1,156 patients diagnosed with CSVD associated WMH (median age, 54 years; 653 males) obtained between September 2018 and September 2019 from Beijing Tiantan Hospital was retrospectively analyzed in this study. Locations of CSVD-WMH on the FLAIR scans were manually marked by two experienced neurologists. Using the manually labeled data of 996 patients (development set), a U-shaped novel 2D convolutional neural network (CNN) architecture was trained for automatic segmentation of CSVD-WMH. The segmentation performance of the network was evaluated with per pixel and lesion level dice scores using an independent internal test set (n = 160) and a multi-center external test set (n = 90, three medical centers). The clinical suitability of the segmentation results, classified as acceptable, acceptable with minor revision, acceptable with major revision, and not acceptable, was analyzed by three independent neuroradiologists. The inter-neuroradiologists agreement rate was assessed by the Kendall-W test.Results: On the internal and external test sets, the proposed CNN architecture achieved per pixel and lesion level dice scores of 0.72 (external test set), and they were significantly better than the state-of-the-art deep learning architectures proposed for WMH segmentation. In the clinical evaluation, neuroradiologists observed the segmentation results for 95% of the patients were acceptable or acceptable with a minor revision.Conclusions: A deep learning system can be used for automated, objective, and clinically meaningful segmentation of CSVD-WMH with high accuracy.

scholarly journals Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography

2021 ◽  
pp. bjophthalmol-2020-316290
Author(s):  
Bing Li ◽  
Huan Chen ◽  
Bilei Zhang ◽  
Mingzhen Yuan ◽  
Xuemin Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning System ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Glaucomatous Optic Neuropathy ◽  
Test Set ◽  
Receiver Operating Characteristic Curves ◽  
Age Related ◽  
Test Sets ◽  
External Test

AimTo explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.MethodsDiagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted.ResultsThe area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity.ConclusionThe proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world.

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