همایش ملی بیوانفورماتیک ایران

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

Predicting drug response using omics data and artificial intelligence approach in cancer

نویسندگان :

Abdolhamid Nazerpanahi¹ Mohammad Ali Hossein Beigy² Amirhossein Keyhanipour³ Kaveh Kavousi⁴ Siavash Kavousi⁵

1- University of Tehran 2- University of Tehran 3- University of Tehran 4- tehran 5- tarbiat modares university

کلمات کلیدی :

Precision oncology،Personalized Medicine،Neural networks،Drug response prediction

چکیده :

Cancer treatments often yield varied responses among patients, highlighting the urgent need for personalized therapeutic approaches. Leveraging recent advances in artificial intelligence (AI) and systems biology, this study introduces a novel AI-driven framework designed to predict how different cancer cell lines respond to specific drugs. Using a robust dataset comprising approximately 5,627,072 data points—including cell line, genomic, transcriptomic, proteomic, and mutation variations sourced from COSMIC, GDSC, and Ensembl databases—we focused on the comprehensive analysis of 3,008 crucial genes across 933 unique cancer cell lines. Our approach employs a fuzzy vector-based method to construct a gene embedding of dimensions (1, 3008) for each cell line, where each element represents a fuzzy value calculated based on the extensive dataset. This fuzzy value indicates the relative importance of gene mutations, providing a more nuanced representation of mutation impacts on drug sensitivity than traditional binary embeddings. To model drug data, we utilized Morgan fingerprints, enabling detailed molecular characterization. By integrating multi-omics (genomic, transcriptomic, and proteomic) data, we accurately modeled each gene’s contribution, reflecting the complexity of molecular interactions and their biological significance. These refined representations were fed into artificial neural networks (ANNs) to predict therapeutic responses to targeted treatments. The results demonstrate that this gene importance-based method, combined with deep learning techniques, achieves a Pearson correlation of 81.12%, which is slightly better than recent work in the field. This work marks a significant step forward in precision oncology, offering a robust, data-driven strategy that not only achieves high predictive accuracy but also elucidates the underlying biological factors influencing drug effectiveness. Ultimately, our framework advances the promise of personalized medicine, tailoring cancer treatments to each patient’s unique molecular profile, thereby improving therapeutic efficacy and minimizing adverse effects.

لیست مقالات

لیست مقالات بایگانی شده

The key role of genes involved in proline biosynthesis and photosynthesis in drought response during Soybean flowering stage

Zeinab Shamabadi - Maryam Pasandideh Arjmand - Mohammad Mohsenzadeh Golfazani - Habibollah Samizadeh lahiji

A computational approach to identify the biomarker based on the RNA sequencing data analysis for Alzheimer’s disease

Atena Vaghf - Shahram Tahmasebian - Nayereh Abdali

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

Masoud Ahmadlou

Elucidating Inflammatory Pathways in ‎‎Polycystic Ovary Syndrome: A ‎‎Transcriptomic Investigation of Granulosa ‎‎Cell Gene Expression

Mobina Afshari Kave - Farinaz Behfarjam - Maryam Shahhosseini - Mostafa Rafiepour ‎ - Zahra Safaei Nezhad

In Silico Design and Evaluation of a Multi-Epitope Vaccine Candidate Against Escherichia coli and Staphylococcus aureus Involved in Bovine Clinical Mastitis

Aryan Ghorbani - Negin Khalili-samani - Maryam Amirinia - Faezeh Jazayeri-soreshjani - Faranak Ravanan - Mohammad Oveysi-rastabi - Abbas Doosti

PPI network construction and Cytoscape analysis for metastatic gastric cancer to ovary

Ameneh Hassani - Ghasem Bagherpour - Ali Salari - Behrooz Johari - Abdolmajid Ghaempanah

Molecular Docking Analysis of Eugenol and Paclitaxel Targeting MRAS in Breast Cancer Therapy

Yaas Rowhani - Somayeh Reiisi

Enhancing the Clustering and Sorting Procedures in the MAGUS Method for Multiple Sequence Alignment

Masih Hajsaeedi - Mohsen Hooshmand

Diagnosis of Diabetic Retinopathy with Fuzzy Technique and Deep Learning

Samaneh Noroozi - Sajad Haghzad Klidbary

Accelerating Diffusion-Based Graph Generative Models for De Novo Drug Design via Hessian Trace Approximation

Negin Bagherpour - AmirHossein Heidari - Alireza Fotouhi Siahpirani

ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 40.4.1