همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Combination therapy synergism prediction for virus treatment using machine learning models

نویسندگان :

Shayan Majidifar¹ Arash Zabihian² Mohsen Hooshmand³

1- IASBS 2- Department of QA, Kimia Zist Parsian Pharmaceutical Company, Zanjan, Iran 3- IASBS

کلمات کلیدی :

Viral disease treatment،Combination therpay،deep learning،machine learning،drug repurposing

چکیده :

Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature.

لیست مقالات

لیست مقالات بایگانی شده

In Silico Design of DNA G-Quadruplex Aptamers Targeting Lipopolysaccharide Core and Capsular Polysaccharide in Multidrug-Resistant Klebsiella pneumoniae

Aida Arezoumandchafi - Maryam Azimzadeh Irani - Hamidreza Mollasalehi

Element-Specific Estimation of Background Mutation Rates in Whole Cancer Genomes Through Transfer Learning

ّFarideh Bahari - Reza Ahangari Cohan - Hesam Montazeri

Comprehensive Analysis of DCLK Family Kinases in Gastrointestinal Cancers and Neoplastic Progression

Zahra Salehi - Leili Rejali - Moein Piroozkhah - Pooya Jalali - Ali Aghajani - Mahsa Saeedi Niyasar - Binazir Khanabadi - Amir Sadeghi - Ehsan Nazemalhossein Mojarad

Using interpretable deep learning models and multi-objective data for computational discovery of new drugs

Masoud Ahmadlou

Drug repurposing using bulk RNA-seq based on key genes involved in inflammatory bowel disease

Nayereh Abdali - Shahram Tahmasebian - Atena Vaghf

Novel Anti-ageing Strategy Via Targeting CST With Vitamin B1

SeyedMobin Mousavi Ghomi - Maryam Azimzadeh Irani - Aida Arezoumandchafi

Tissue-Specific Gene Co-Expression Analysis in Pediatric Ependymomas Across Different Anatomical Regions

Zahra Eghbali - Mohammad Reza Zabihi - Pedram Jahangiri - Zahra Salehi

Exploring the Regulatory Landscape of LncRNAs in Alzheimer’s Disease: Insights into Inflammation

Narjes Khatoun Shabani Sadr - Farideh Faramarzi - Mehrdad Behmanesh

Attention based Graph Neural Network for Identifying Coding and Non-coding Breast Cancer Drivers

Bahar Mahdavi - Mitra Nemati Andavari - Mehdi Rajabizadeh - Mansoor Rezghi

Dissecting the impact of smoking on epigenetic mechanisms that influencing Lung cancer susceptibility

Mahboobeh Golchinpour - Alireza Fotuhi Siahpirani

بیشتر