1 citations · 2 across the 4 of their papers we have counts for
4 papers
Modeling Dabrafenib Response Using Multi-Omics Modality Fusion and Protein Network Embeddings Based on Graph Convolutional Networks
La Ode Aman, A Mu'thi Andy Suryadi, Dizky Ramadani Putri Papeo +4
Cancer cell response to targeted therapy arises from complex molecular interactions, making single omics insufficient for accurate prediction. This study develops a model to predic…
Prediction of PLX-4720 Sensitivity in Cancer Cell Lines through Multi-Omics Integration and Attention-Based Fusion Modeling
La Ode Aman, Arfan Arfan, Aiyi Asnaw +3
Predicting the sensitivity of cancer cell lines to PLX-4720, a preclinical BRAF inhibitor, requires models capable of capturing the multilayered regulation of oncogenic signaling.…
Prediction of Binding Affinity for ErbB Inhibitors Using Deep Neural Network Model with Morgan Fingerprints as Features
La Ode Aman
The ErbB receptor family, including EGFR and HER2, plays a crucial role in cell growth and survival and is associated with the progression of various cancers such as breast and lun…
AI Model for Predicting Binding Affinity of Antidiabetic Compounds Targeting PPAR
La Ode Aman, Aiyi Asnawi
This study aims to develop a deep learning model for predicting the binding affinity of ligands targeting the Peroxisome Proliferator-Activated Receptor (PPAR) family, using 2D mol…