Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach

Zhang, Hui; Liu, Chun‐Tao; Mao, Jun; Shen, Chen; Xie, Ruiling; Mu, Bo

doi:10.1016/j.tiv.2020.104812

Cited by 26 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Despite the power of in silico approaches to identify emerging toxins, we know of only two published studies that have developed algorithms for ototoxicity ( 17 , 18 ). Both used a Bayesian approach and trained their models on a dataset of ototoxins and non-ototoxins.…”

Section: Models For Ototoxicity Studiesmentioning

confidence: 99%

“…Both models provide an objective system for screening without a priori assumptions about ototoxic potential and for testing multiple drug combinations (12,13). We then discuss the untapped potential of in silico screening using computational models; an approach widely used for toxicity screening in other tissues such as the heart and liver (14)(15)(16) but rarely for ototoxin identification (17,18). We focus on the strategic use of these systems and how researchers can employ these biological and computational models for identifying novel ototoxins.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Coffin

Boney²,

Hill

et al. 2021

Front. Neurol.

View full text Add to dashboard Cite

Over 100 drugs and chemicals are associated with permanent hearing loss, tinnitus, and vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely assessed in pre-clinical drug development or during clinical trials, so this debilitating side-effect is often discovered as patients begin to report hearing loss. Furthermore, drug-induced ototoxicity in adults, and particularly in elderly patients, may go unrecognized due to hearing loss from a variety of etiologies because of a lack of baseline assessments immediately prior to novel therapeutic treatment. During the current pandemic, there is an intense effort to identify new drugs or repurpose FDA-approved drugs to treat COVID-19. Several potential COVID-19 therapeutics are known ototoxins, including chloroquine (CQ) and lopinavir-ritonavir, demonstrating the necessity to identify ototoxic potential in existing and novel medicines. Furthermore, several factors are emerging as potentiators of ototoxicity, such as inflammation (a hallmark of COVID-19), genetic polymorphisms, and ototoxic synergy with co-therapeutics, increasing the necessity to evaluate a drug's potential to induce ototoxicity under varying conditions. Here, we review the potential of COVID-19 therapies to induce ototoxicity and factors that may compound their ototoxic effects. We then discuss two models for rapidly detecting the potential for ototoxicity: mammalian auditory cell lines and the larval zebrafish lateral line. These models offer considerable value for pre-clinical drug development, including development of COVID-19 therapies. Finally, we show the validity of in silico screening for ototoxic potential using a computational model that compares structural similarity of compounds of interest with a database of known ototoxins and non-ototoxins. Preclinical screening at in silico, in vitro, and in vivo levels can provide an earlier indication of the potential for ototoxicity and identify the subset of candidate therapeutics for treating COVID-19 that need to be monitored for ototoxicity as for other widely-used clinical therapeutics, like aminoglycosides and cisplatin.

show abstract

Section: Models For Ototoxicity Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Coffin

Boney²,

Hill

et al. 2021

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…di mana P (c) mewakili probabilitas sebelumnya, P (F) adalah probabilitas marginal, P (c | F) adalah probabilitas posterior kelas gabungan, P (F | c) menunjukkan probabilitas bersyarat, masingmasing [18]. Klasifikasi naïve bayes perlu untuk menyelesaikan satu set estimasi kepadatan satu dimensi.…”

Section: Naive Bayesunclassified

Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest

Apriliah¹,

Kurniawan

Baydhowi³

et al. 2021

SISTEMASI

View full text Add to dashboard Cite

Diabetes adalah salah satu penyakit kronis yang mengancam jiwa dengan pertumbuhan tercepat yang telah mempengaruhi 422 juta orang di seluruh dunia menurut laporan Organisasi Kesehatan Dunia (WHO), pada tahun 2018. Diabetes dianggap sebagai salah satu penyakit paling mematikan dan kronis yang menyebabkan peningkatan gula darah. Banyak komplikasi terjadi jika diabetes tetap tidak diobati dan tidak teridentifikasi. Namun, peningkatan pendekatan machine learning memecahkan masalah kritis ini. Tujuan dari penelitian ini adalah merancang model yang dapat memprakirakan kemungkinan terjadinya diabetes pada pasien dengan ketelitian yang maksimal. Klasifikasi adalah teknik data mining yang menetapkan kategori pada kumpulan data untuk membantu dalam memprediksi dan analisis yang lebih akurat. Oleh karena itu tiga algoritma klasifikasi machine learning yaitu Suport Vector Machine, Naive Bayes dan Random Forest digunakan dalam percobaan ini untuk mendeteksi diabetes secara dini. Eksperimen dilakukan menggunakan dataset Diabetes Hospital in Sylhet, Bangladesh yang bersumber dari UCI repository. Performa ketiga algoritma dievaluasi pada berbagai ukuran seperti Precision, Accuracy, F-Measure, dan Recall. Akurasi diukur melalui instance yang diklasifikasikan dengan benar dan salah. Hasil yang diperoleh menunjukkan Random Forest mengungguli dengan nilai akurasi tertinggi 97,88% dibandingkan algoritma lain. Hasil ini diverifikasi menggunakan kurva Receiver Operating Characteristic (ROC) secara tepat dan sistematis.

show abstract

“…Multiple models are used to train the classifier for the same batch of data, and the final ensemble classifier is obtained by using the ensemble learning algorithm. At the same time, 6 common machine learning classification algorithms were selected for comparative experiments, including Naive Bayes (NB) [15] , Support Vector Machine (SVM) [16,17] , Logistic Regression (LR) [18] , Multilayer Perceptron (MLP) [19] , Deep Forest (GCForest) [20] , eXtreme Gradient boosting (XGBoost) [21] .…”

Section: Ensemble Analysis Of Clinical Datamentioning

confidence: 99%

Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning

Dai¹,

Meng

Tan

et al. 2020

Preprint

View full text Add to dashboard Cite

Background and purpose: Artificial intelligence is an important product of the rapid development of computer technology today. This study intends to propose an intelligent diagnosis and detection method for AR based on ensemble learning. Method: This study collectedAR cases and other 7 types of diseases with similar symptoms:Rhinosinusitis, Chronic rhinitis, upper respiratory tract infection etc.) and collected clinical data such as medical history, clinical symptoms, allergen detection and imaging. Multiple models are used to train the classifier for the same batch of data, and the final ensemble classifier is obtained by using the ensemble learning algorithm. 5 common machine learning classification algorithms were selected for comparative experiments, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), Deep Forest (GCForest), eXtreme Gradient boosting (XGBoost). In order to evaluate the prediction results of AR samples, parameters such as Precision, Sensitivity, Specificity, G-Mean, F1-Score, and AUC under the ROC curve are jointly used as prediction evaluation indicators. Results: 7 classification models are used for comparison, covering probability model, tree model, linear model, ensemble model and neural network models, and the comprehensive classification evaluation index is lower than the ensemble classification algorithms ARF-OOBEE and GCForest. Compared with other algorithms, the accuracy of G-Mean and AUC parameters is improved nearly 2%, and it has good comprehensive classification characteristics for massive large data and unbalanced samples. Conclusion: The ensemble learning ARF-OOBEE model has good generalization performance and comprehensive classification ability to be used for diagnosis of AR.

show abstract

Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach

Cited by 26 publications

References 23 publications

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest

Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning

Contact Info

Product

Resources

About