Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel: Development and usability study

BACKGROUND

Breast cancer (BC) is the most common malignant cancer in women. A predictive model is required to predict the 5-year survival in patients with BC (5YSPBC) and improve the treatment quality by increasing their survival rate. However, no reports in literature about apps developed and designed in medical practice to classify the 5YSPBC. This study aimed to build a model to develop an app for an automatically accurate classification of the 5YSPBC.

METHODS

A total of 1810 patients with BC were recruited in a hospital in Taiwan from the secondary data with codes on 53 characteristic variables that were endorsed by professional staff clerks as of December 31, 2019. Five models (i.e., revolution neural network [CNN], artificial neural network, Naïve Bayes, K-nearest Neighbors Algorithm, and Logistic regression) and 3 tasks (i.e., extraction of feature variables, model comparison in accuracy [ACC] and stability, and app development) were performed to achieve the goal of developing an app to predict the 5YSPBC. The sensitivity, specificity, and receiver operating characteristic curve (area under ROC curve) on models across 2 scenarios of training (70%) and testing (30%) sets were compared. An app predicting the 5YSPBC was developed involving the model estimated parameters for a website assessment.

RESULTS

We observed that the 15-variable CNN model yields higher ACC rates (0.87 and 0.86) with area under ROC curves of 0.80 and 0.78 (95% confidence interval 0.78-82 and 0.74-81) based on 1357 training and 540 testing cases an available app for patients predicting the 5YSPBC was successfully developed and demonstrated in this study.

CONCLUSION

The 15-variable CNN model with 38 parameters estimated using CNN for improving the ACC of the 5YSPBC has been particularly demonstrated in Microsoft Excel. An app developed for helping clinicians assess the 5YSPBC in clinical settings is required for application in the future.

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players’ data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (≥15 years). Featured variables were taken from the higher correlation coefficients (<0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model’s parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93–0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87–0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players.

CYP1A2, 2A13, and 3A4 network and interaction with aflatoxin B1

Aspergillus fungi are known to produce aflatoxins, among which aflatoxin B1 (AFB1) is the most potent carcinogen that is metabolised by cytochrome P450 (CYP450). In the liver, AFB1 is metabolised into exo-8,9-epoxide by the CYP1A2 enzymes. The resulting epoxide can react with guanine to cause DNA damage. Natural inhibitors are being identified. However, the modes of action are poorly understood. In the current study, we have investigated the mode of action of AFB1 with CYP1A2, CYP3A4 and CYP2A13 using molecular dynamic simulation (MD simulation) approaches. The interaction network and paths among CYP1A2, CYP3A4, and CYP2A13 have been investigated using the STRING database and PathLinker plugin of Cytoscape. CYP3A4 is the most active protein involved in interactions with AFB1 during its metabolism. Residues 362ARG, 445SER, 450LEU and 451PHE of CYP1A2 are important, interacting with AFB1 and converting it to toxic exo-AFB1-8,9-epoxide (AFBEX). The pathway shows that microsomal epoxide hydrolase (EPHX1) may acts as initiator in the signalling pathway where CYP1A2, CYP3A4 and CYP2A13 interact in a sequential order. The interaction network shows there to be a strong association in expression among CYP1A2, CYP3A4 and CYP2A13 along with other metabolising enzymes. The complex of AFB1 and CYP1A2 was found to be stable during the MD simulation. This study provides a better understanding of the mode of action between AFB1 and CYP1A2, CYP3A4 and CYP2A13 which relates to the effective management of AFB1 toxicity. EPHX1 in the protein network may be an ideal target when designing inhibitors to prevent the toxin’s activation. Peptide inhibitors may be designed to block the substrate site residues of CYP1A2 in order to prevent the conversion from AFB1 into AFBEX. This would either neutralise or reduce its toxicity.

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Background:

Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450.

Objective:

This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates.

Results:

Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors.

Conclusion:

This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces

The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly introduced to predict DNA-binding residues on surface of DNA-binding proteins, and the merits and limitations of these methods are mainly discussed. This chapter focuses on the structure-based approaches and mainly discusses the framework of machine learning methods in application to DNA-binding prediction task.