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Lebanese American University

Academic institutionasia · lb
Official website
Research library11linked papers
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Selected work

Representative Papers

Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models

Sep 28, 2026

This study addresses the limitation of existing drug-target interaction (DTI) models that independently encode sequences and struggle to explicitly model cross-molecular dependencies. Inspired by the induced-fit mechanism, we propose a bidirectional cross-attention framework. Methodologically, it integrates ChemBERTa and ESM-2 pretrained representations, employing hierarchical sequential cross-attention to enable fine-grained interactions between chemical substructures and protein regions. One-dimensional convolutions and attention pooling are then utilized to construct fixed-size interaction vectors. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on the BIOSNAP dataset and matches SOTA AUROC on the Davis dataset. Notably, with only 25.2M parameters, it maintains strong competitiveness while exhibiting superior cold-start generalization capabilities.

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BERT4DTI : BERT-based Model for Predicting Drug-Protein Interactions

Sep 27, 2026

This study addresses the challenges of scarce labeled data, high fine-tuning costs, and the lack of interactive dependencies in independent sequence encoding for drug-target prediction by proposing an efficient predictive framework. The method employs ChemBERTa and ProtBERT as encoders and introduces a bidirectional cross-attention mechanism to capture specific dependencies between drug-protein pairs, followed by convolutional layers and a multilayer perceptron for classification. Additionally, a truncated layer strategy is adopted to substantially reduce the number of trainable parameters. Experimental results demonstrate that the proposed model achieves state-of-the-art ROC-AUC and sensitivity across multiple benchmarks while reducing the total parameter count to 125 million, thereby realizing an excellent balance between predictive performance and computational efficiency.

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New Approximations of Non-Separable MIMO Channels by Separable Channels for Accurate Ergodic Capacity Analysis

Aug 15, 2026

This study addresses the analytical complexity and absence of closed-form ergodic capacity solutions in Weichselberger models arising from their non-separable structure. To overcome this, we propose a KL-divergence-based rank-1 decomposition combined with a novel moment-matching method that accurately maps non-separable channels onto separable models. Closed-form capacity expressions are derived across the entire signal-to-noise ratio (SNR) regime, effectively mitigating limitations of conventional approaches under sparse scattering and low-SNR conditions. Results demonstrate that the proposed model achieves significantly higher accuracy than traditional Kronecker models, providing an efficient and analytically tractable theoretical framework for complex MIMO channel analysis.

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DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

Jul 13, 2026

This work addresses the challenges of scarce annotations and extreme class imbalance (seizure segments constituting less than 10%) in EEG-based epilepsy detection by proposing the first self-supervised foundation model based on denoising diffusion. The approach employs a 1D U-Net architecture augmented with multi-head self-attention for pretraining on large-scale unlabeled EEG data to learn generalizable neural representations. It further introduces an innovative policy gradient reinforcement learning fine-tuning mechanism that directly optimizes the clinically critical F1 score. Under strict patient-wise evaluation, the model achieves 59% F1 on a four-class seizure subtype classification task, 85% weighted F1 and 59% seizure recall on binary detection, and 97.6% segment-level accuracy, substantially reducing reliance on labeled data while enhancing sensitivity to rare seizure events.

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Recent publications

Latest Papers

Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models

Sep 28, 2026

This study addresses the limitation of existing drug-target interaction (DTI) models that independently encode sequences and struggle to explicitly model cross-molecular dependencies. Inspired by the induced-fit mechanism, we propose a bidirectional cross-attention framework. Methodologically, it integrates ChemBERTa and ESM-2 pretrained representations, employing hierarchical sequential cross-attention to enable fine-grained interactions between chemical substructures and protein regions. One-dimensional convolutions and attention pooling are then utilized to construct fixed-size interaction vectors. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on the BIOSNAP dataset and matches SOTA AUROC on the Davis dataset. Notably, with only 25.2M parameters, it maintains strong competitiveness while exhibiting superior cold-start generalization capabilities.

0 citationsRead paper

BERT4DTI : BERT-based Model for Predicting Drug-Protein Interactions

Sep 27, 2026

This study addresses the challenges of scarce labeled data, high fine-tuning costs, and the lack of interactive dependencies in independent sequence encoding for drug-target prediction by proposing an efficient predictive framework. The method employs ChemBERTa and ProtBERT as encoders and introduces a bidirectional cross-attention mechanism to capture specific dependencies between drug-protein pairs, followed by convolutional layers and a multilayer perceptron for classification. Additionally, a truncated layer strategy is adopted to substantially reduce the number of trainable parameters. Experimental results demonstrate that the proposed model achieves state-of-the-art ROC-AUC and sensitivity across multiple benchmarks while reducing the total parameter count to 125 million, thereby realizing an excellent balance between predictive performance and computational efficiency.

0 citationsRead paper

New Approximations of Non-Separable MIMO Channels by Separable Channels for Accurate Ergodic Capacity Analysis

Aug 15, 2026

This study addresses the analytical complexity and absence of closed-form ergodic capacity solutions in Weichselberger models arising from their non-separable structure. To overcome this, we propose a KL-divergence-based rank-1 decomposition combined with a novel moment-matching method that accurately maps non-separable channels onto separable models. Closed-form capacity expressions are derived across the entire signal-to-noise ratio (SNR) regime, effectively mitigating limitations of conventional approaches under sparse scattering and low-SNR conditions. Results demonstrate that the proposed model achieves significantly higher accuracy than traditional Kronecker models, providing an efficient and analytically tractable theoretical framework for complex MIMO channel analysis.

0 citationsRead paper

DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

Jul 13, 2026

This work addresses the challenges of scarce annotations and extreme class imbalance (seizure segments constituting less than 10%) in EEG-based epilepsy detection by proposing the first self-supervised foundation model based on denoising diffusion. The approach employs a 1D U-Net architecture augmented with multi-head self-attention for pretraining on large-scale unlabeled EEG data to learn generalizable neural representations. It further introduces an innovative policy gradient reinforcement learning fine-tuning mechanism that directly optimizes the clinically critical F1 score. Under strict patient-wise evaluation, the model achieves 59% F1 on a four-class seizure subtype classification task, 85% weighted F1 and 59% seizure recall on binary detection, and 97.6% segment-level accuracy, substantially reducing reliance on labeled data while enhancing sensitivity to rare seizure events.

0 citationsRead paper