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

Computational Analysis of the D92V Mutation in the EF-hand II Loop of the Mnemiopsin 2 Photoprotein

Hanieh Ramezani - Vahab Jafarian - Akram Shirdel - Khosrow Khalifeh - Fatemeh Khatami

Identification of Driver Genes in Glioblastoma Based on Single-Cell Gene Expression Data Using Integrated Pseudotime and Phylogenetic Analysis

Fateme Mirza-Abolhassani - Sobhan Ahmadian Moghadam - Fatemeh Zare-Mirakabad - Kaveh Kavousi

Modeling and Predicting the Use of Medications Antiplatelets and ARBs Using Logistic Regression

Ahmad Aliyari Boroujeni - Pouya Joze Soleimani - Shima Soltani - Farzaneh Karamitanha

Iron, Oxidative Stress, and Skin Aging: The Therapeutic Potential of TGF-β1 Modulation

Maryam Kaboli - Amir Amiri-Yekta - Seyed Abolhassan Shahzadeh Fazeli - Yasser Tahamtani

Applying immunoinformatics methods for Multiepitope Vaccine Design against HIV virus Based on the INT, RT, PRO genes

Fatemeh Hassanzadeh - Zahra Hassanzadeh - Ava Hashempour

ATIC as a Theranostic Biomarker in Gastrointestinal Cancers: Insights from Autophagy Pathway Analysis

Pooya Jalali - Maryam Eghbali - Zahra Eghabli - Yasaman Gholinezhad - Zahra Salehi

Simultaneous overexpression of CD70 and downregulation of CD84 as a prognostic marker for glucocorticoid resistance in B cell Acute lymphoblastic leukemia

Mohammad Hossein Shakib Manesh - Soheila Rahgozar

Exploring the Anticancer Potential of Flavonoids from Morus alba Against Breast Cancer: An In Silico Approach

Kimia Asadi - Negar Ghaleh Navi - Ehsan Karimi

Comprehensive Gene and Protein Catalog for Antimicrobial Environments: A Metagenomic Approach to Mitigate Antimicrobial Resistance

Donya Afshar Jahanshahi - Arad Ariaeenejad - Arman Hasannejad - Mohammad Reza Zabihi - Shohreh Ariaeenejad - Kaveh Kavousi

Analysis of enrichment pathways and ontology of genes related to Feed efficiency in sheep

Mehre Mohammadnezhad - Mohsen Gholizadeh

بیشتر