Nhat Truong Pham

(†) denotes equal contribution

(*) denotes correspondance

denotes journal

denotes conference

2026

1. ADVEI

   

   Federated learning for medical image analysis: Methods, challenges, and future directions

   Thu Thuy Le , Phuong-Nam Tran , Nhat Truong Pham , Balachandran Manavalan , Li Shen , Choong Seon Hong , and Duc Ngoc Minh Dang

   Advanced Engineering Informatics, 2026

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   In recent years, rapid technological advances have made artificial intelligence a global trend, significantly advancing the fields of medicine and healthcare. Meanwhile, concerns about information security and medical data privacy have also increased. Although many studies have collected data from hospitals to train centralized models, data sharing between medical facilities is often restricted and protected by law. Federated learning (FL) emerges as a promising solution, enabling model training across different medical facilities without sharing raw data, thereby safeguarding patient privacy and ensuring data security. Our study reviews key works on FL in medical image analysis (MIA) from 2020 to 2025. We systematically analyze major challenges, including data heterogeneity, non-independent and identically distributed data, missing labels, communication costs, and privacy risks during model updates. Additionally, this review covers emerging approaches such as semi-supervised learning, unsupervised learning, domain shift learning, advanced deep learning architectures, and personalized models within the context of FL. We aim to provide a comprehensive overview of the current research landscape, methods for addressing these challenges, and future directions for FL in MIA.

   @article{le2026federated,
     title = {Federated learning for medical image analysis: Methods, challenges, and future directions},
     author = {Le, Thu Thuy and Tran, Phuong-Nam and Pham, Nhat Truong and Manavalan, Balachandran and Shen, Li and Hong, Choong Seon and Dang, Duc Ngoc Minh},
     journal = {Advanced Engineering Informatics},
     volume = {76},
     pages = {104976},
     year = {2026},
     publisher = {Elsevier},
     doi = {10.1016/j.aei.2026.104976},
   }

2025

1. JCheminf

   

   xBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides

   Nguyen Doan Hieu Nguyen^† , Nhat Truong Pham^† , Duong Thanh Tran , Leyi Wei , Adeel Malik , and Balachandran Manavalan

   Journal of Cheminformatics, 2025

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   Bitter peptides (BPs), derived from the hydrolysis of proteins in food, play a crucial role in both food science and biomedicine by influencing taste perception and participating in various physiological processes. Accurate identification of BPs is essential for understanding food quality and potential health impacts. Traditional machine learning approaches for BP identification have relied on conventional feature descriptors, achieving moderate success but struggling with the complexities of biological sequence data. Recent advances utilizing protein language model embedding and meta-learning approaches have improved the accuracy, but frequently neglect the molecular representations of peptides and lack interpretability. In this study, we propose xBitterT5, a novel multimodal and interpretable framework for BP identification that integrates pretrained transformer-based embeddings from BioT5+ with the combination of peptide sequence and its SELFIES molecular representation. Specifically, incorporating both peptide sequences and their molecular strings, xBitterT5 demonstrates superior performance compared to previous methods on the same benchmark datasets. Importantly, the model provides residue-level interpretability, highlighting chemically meaningful substructures that significantly contribute to its bitterness, thus offering mechanistic insights beyond black-box predictions. A user-friendly web server (https://balalab-skku.org/xBitterT5/) and a standalone version (https://github.com/cbbl-skku-org/xBitterT5/) are freely available to support both computational biologists and experimental researchers in peptide-based food and biomedicine.

   Scientific Contribution

   We propose xBitterT5, a novel multimodal transformer-based framework for the identification of BPs. By utilizing the pretrained BioT5+ model, xBitterT5 effectively extracts high-level representations from both the peptide sequences and their corresponding SELFIES molecular representation. This dual-modality approach enables a more comprehensive understanding of the peptide sequence by leveraging its molecular string, leading to substantial improvements in performance across two benchmark datasets. Additionally, xBitterT5 offers interpretability by identifying key molecular substructures that contribute to bitterness, thereby providing mechanistic insights essential for peptide-based food and drug applications.

   @article{nguyen2025xbittert5,
     title = {xBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides},
     author = {Nguyen, Nguyen Doan Hieu and Pham, Nhat Truong and Tran, Duong Thanh and Wei, Leyi and Malik, Adeel and Manavalan, Balachandran},
     journal = {Journal of Cheminformatics},
     volume = {17},
     number = {1},
     pages = {127},
     year = {2025},
     publisher = {Springer},
     doi = {10.1186/s13321-025-01078-1},
   }

2. JPA

   

   HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors

   Duong Thanh Tran^† , Nhat Truong Pham^† , Nguyen Doan Hieu Nguyen , Leyi Wei , and Balachandran Manavalan

   Journal of Pharmaceutical Analysis, 2025

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   Peptide-based therapeutics hold great promise for the treatment of various diseases; however, their clinical application is often hindered by toxicity challenges. The accurate prediction of peptide toxicity is crucial for designing safe peptide-based therapeutics. While traditional experimental approaches are time-consuming and expensive, computational methods have emerged as viable alternatives, including similarity-based and machine learning (ML)-/deep learning (DL)-based methods. However, existing methods often struggle with robustness and generalizability. To address these challenges, we propose HyPepTox-Fuse, a novel framework that fuses protein language model (PLM)-based embeddings with conventional descriptors. HyPepTox-Fuse integrates ensemble PLM-based embeddings to achieve richer peptide representations by leveraging a cross-modal multi-head attention mechanism and Transformer architecture. A robust feature ranking and selection pipeline further refines conventional descriptors, thus enhancing prediction performance. Our framework outperforms state-of-the-art methods in cross-validation and independent evaluations, offering a scalable and reliable tool for peptide toxicity prediction. Moreover, we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse, highlighting its effectiveness in enhancing model performance. Furthermore, the HyPepTox-Fuse server is freely accessible at https://balalab-skku.org/HyPepTox-Fuse/ and the source code is publicly available at https://github.com/cbbl-skku-org/HyPepTox-Fuse/. The study thus presents an intuitive platform for predicting peptide toxicity and supports reproducibility through openly available datasets.

   @article{tran2025hypeptox,
     title = {HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors},
     author = {Tran, Duong Thanh and Pham, Nhat Truong and Nguyen, Nguyen Doan Hieu and Wei, Leyi and Manavalan, Balachandran},
     journal = {Journal of Pharmaceutical Analysis},
     volume = {15},
     number = {8},
     pages = {101410},
     year = {2025},
     publisher = {Elsevier},
     doi = {10.1016/j.jpha.2025.101410},
   }

2024

1. LPM@ACL

   

   Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion

   Duong Thanh Tran^† , Nhat Truong Pham^† , Nguyen Doan Hieu Nguyen , and Balachandran Manavalan

   In Proceedings of the 1st Workshop on Language + Molecules (L+M 2024), 2024

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   This paper introduces Mol2Lang-VLM, an enhanced method for refining generative pre-trained language models for molecule captioning using multimodal features to achieve more accurate caption generation. Our approach leverages the encoder and decoder blocks of the Transformer-based architecture by introducing third sub-layers into both. Specifically, we insert sub-layers in the encoder to fuse features from SELFIES strings and molecular images, while the decoder fuses features from SMILES strings and their corresponding descriptions. Moreover, cross multi-head attention is employed instead of common multi-head attention to enable the decoder to attend to the encoder’s output, thereby integrating the encoded contextual information for better and more accurate caption generation. Performance evaluation on the CheBI-20 and L+M-24 benchmark datasets demonstrates Mol2Lang-VLM’s superiority, achieving higher accuracy and quality in caption generation compared to existing methods. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/mol2lang/.

   @inproceedings{tran2024mollangvlm,
     title = {Mol2Lang-{VLM}: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion},
     author = {Tran, Duong Thanh and Pham, Nhat Truong and Nguyen, Nguyen Doan Hieu and Manavalan, Balachandran},
     booktitle = {Proceedings of the 1st Workshop on Language + Molecules L+M 2024},
     pages = {97--102},
     year = {2024},
     doi = {10.18653/v1/2024.langmol-1.12},
   }

2. LPM@ACL

   

   Lang2Mol-Diff: A Diffusion-Based Generative Model for Language-to-Molecule Translation Leveraging SELFIES Molecular String Representation

   Nguyen Doan Hieu Nguyen^† , Nhat Truong Pham^† , Duong Thanh Tran , and Balachandran Manavalan

   In Proceedings of the 1st Workshop on Language + Molecules (L+M 2024), 2024

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   Generating de novo molecules from textual descriptions is challenging due to potential issues with molecule validity in SMILES representation and limitations of autoregressive models. This work introduces Lang2Mol-Diff, a diffusion-based language-to-molecule generative model using the SELFIES representation. Specifically, Lang2Mol-Diff leverages the strengths of two state-of-the-art molecular generative models: BioT5 and TGM-DLM. By employing BioT5 to tokenize the SELFIES representation, Lang2Mol-Diff addresses the validity issues associated with SMILES strings. Additionally, it incorporates a text diffusion mechanism from TGM-DLM to overcome the limitations of autoregressive models in this domain. To the best of our knowledge, this is the first study to leverage the diffusion mechanism for text-based de novo molecule generation using the SELFIES molecular string representation. Performance evaluation on the L+M-24 benchmark dataset shows that Lang2Mol-Diff outperforms all existing methods for molecule generation in terms of validity. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/lang2mol/.

   @inproceedings{nguyen2024langmoldiff,
     title = {Lang2Mol-Diff: A Diffusion-Based Generative Model for Language-to-Molecule Translation Leveraging SELFIES Molecular String Representation},
     author = {Nguyen, Nguyen Doan Hieu and Pham, Nhat Truong and Tran, Duong Thanh and Manavalan, Balachandran},
     booktitle = {Proceedings of the 1st Workshop on Language + Molecules L+M 2024},
     pages = {128--134},
     year = {2024},
     doi = {10.18653/v1/2024.langmol-1.15},
   }

3. MTNA

   

   ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning

   Nhat Truong Pham , Annie Terrina Terrance , Young-Jun Jeon , Rajan Rakkiyappan , and Balachandran Manavalan

   Molecular Therapy-Nucleic Acids, 2024

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   RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences. In ac4C-AFL, we identified the optimal sequence length for model building and implemented an adaptive feature representation strategy that is capable of extracting the most representative features from RNA. To identify the most relevant features, we proposed a novel ensemble feature importance scoring strategy to rank features effectively. We then used this information to conduct the sequential forward search, which individually determine the optimal feature set from the 16 sequence-derived feature descriptors. Utilizing these optimal feature descriptors, we constructed 176 baseline models using 11 popular classifiers. The most efficient baseline models were identified using the two-step feature selection approach, whose predicted scores were integrated and trained with the appropriate classifier to develop the final prediction model. Our rigorous cross-validations and independent tests demonstrate that ac4C-AFL surpasses contemporary tools in predicting ac4C sites. Moreover, we have developed a publicly accessible web server at https://balalab-skku.org/ac4C-AFL/.

   @article{pham2024ac4c,
     title = {ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning},
     author = {Pham, Nhat Truong and Terrance, Annie Terrina and Jeon, Young-Jun and Rakkiyappan, Rajan and Manavalan, Balachandran},
     journal = {Molecular Therapy-Nucleic Acids},
     volume = {35},
     number = {2},
     pages = {102192},
     year = {2024},
     publisher = {Elsevier},
     doi = {10.1016/j.omtn.2024.102192},
   }

4. BiB

   

   H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA

   Nhat Truong Pham , Rajan Rakkiyappan , Jongsun Park , Adeel Malik , and Balachandran Manavalan

   Briefings in Bioinformatics, 2024

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   2’-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.

   @article{pham2024h2opred,
     title = {H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA},
     author = {Pham, Nhat Truong and Rakkiyappan, Rajan and Park, Jongsun and Malik, Adeel and Manavalan, Balachandran},
     journal = {Briefings in Bioinformatics},
     volume = {25},
     number = {1},
     pages = {bbad476},
     year = {2024},
     publisher = {Oxford University Press},
     doi = {10.1093/bib/bbad476},
   }

5. BiB

   

   Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach

   Nhat Truong Pham^† , Le Thi Phan^† , Jimin Seo , Yeonwoo Kim , Minkyung Song , Sukchan Lee , Young-Jun Jeon , and Balachandran Manavalan

   Briefings in Bioinformatics, 2024

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   The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing coronavirus disease 2019 (COVID-19) crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos/. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.

   @article{pham2024advancing,
     title = {Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach},
     author = {Pham, Nhat Truong and Phan, Le Thi and Seo, Jimin and Kim, Yeonwoo and Song, Minkyung and Lee, Sukchan and Jeon, Young-Jun and Manavalan, Balachandran},
     journal = {Briefings in Bioinformatics},
     volume = {25},
     number = {1},
     pages = {bbad433},
     year = {2024},
     publisher = {Oxford University Press},
     doi = {10.1093/bib/bbad433},
   }

2023

1. ESWA

   

   Hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition

   Nhat Truong Pham , Duc Ngoc Minh Dang , Ngoc Duy Nguyen , Thanh Thi Nguyen , Hai Nguyen , Balachandran Manavalan , Chee Peng Lim , and Sy Dzung Nguyen

   Expert Systems with Applications, 2023

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   Recently, speech emotion recognition (SER) has become an active research area in speech processing, particularly with the advent of deep learning (DL). Numerous DL-based methods have been proposed for SER. However, most of the existing DL-based models are complex and require a large amounts of data to achieve a good performance. In this study, a new framework of deep attention-based dilated convolutional-recurrent neural networks coupled with a hybrid data augmentation method was proposed for addressing SER tasks. The hybrid data augmentation method constitutes an upsampling technique for generating more speech data samples based on the traditional and generative adversarial network approaches. By leveraging both convolutional and recurrent neural networks in a dilated form along with an attention mechanism, the proposed DL framework can extract high-level representations from three-dimensional log Mel spectrogram features. Dilated convolutional neural networks acquire larger receptive fields, whereas dilated recurrent neural networks overcome complex dependencies as well as the vanishing and exploding gradient issues. Furthermore, the loss functions are reconfigured by combining the SoftMax loss and the center-based losses to classify various emotional states. The proposed framework was implemented using the Python programming language and the TensorFlow deep learning library. To validate the proposed framework, the EmoDB and ERC benchmark datasets, which are imbalanced and/or small datasets, were employed. The experimental results indicate that the proposed framework outperforms other related state-of-the-art methods, yielding the highest unweighted recall rates of 88.03 ± 1.39 (%) and 66.56 ± 0.67 (%) for the EmoDB and ERC datasets, respectively.

   @article{pham2023hybrid,
     title = {Hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition},
     author = {Pham, Nhat Truong and Dang, Duc Ngoc Minh and Nguyen, Ngoc Duy and Nguyen, Thanh Thi and Nguyen, Hai and Manavalan, Balachandran and Lim, Chee Peng and Nguyen, Sy Dzung},
     journal = {Expert Systems with Applications},
     pages = {120608},
     year = {2023},
     publisher = {Elsevier},
     doi = {10.1016/j.eswa.2023.120608},
   }

2. ESWA

   

   Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds

   Long H. Nguyen^† , Nhat Truong Pham^†* , Van Huong Do , Liu Tai Nguyen , Thanh Tin Nguyen , Hai Nguyen , Ngoc Duy Nguyen , Thanh Thi Nguyen , Sy Dzung Nguyen , Asim Bhatti , and Chee Peng Lim

   Expert Systems with Applications, 2023

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   COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

   @article{nguyen2023fruit,
     title = {Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds},
     author = {Nguyen, Long H. and Pham, Nhat Truong and Do, Van Huong and Nguyen, Liu Tai and Nguyen, Thanh Tin and Nguyen, Hai and Nguyen, Ngoc Duy and Nguyen, Thanh Thi and Nguyen, Sy Dzung and Bhatti, Asim and Lim, Chee Peng},
     journal = {Expert Systems with Applications},
     volume = {213},
     pages = {119212},
     year = {2023},
     publisher = {Elsevier},
     doi = {10.1016/j.eswa.2022.119212},
   }

3. APIN

   

   Towards designing a generic and comprehensive deep reinforcement learning framework

   Ngoc Duy Nguyen , Thanh Thi Nguyen , Nhat Truong Pham , Hai Nguyen , Dang Tu Nguyen , Thanh Dang Nguyen , Chee Peng Lim , Michael Johnstone , Asim Bhatti , Douglas Creighton , and Saeid Nahavandi

   Applied Intelligence, 2023

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   Reinforcement learning (RL) has emerged as an effective approach for building an intelligent system, which involves multiple self-operated agents to collectively accomplish a designated task. More importantly, there has been a renewed focus on RL since the introduction of deep learning that essentially makes RL feasible to operate in high-dimensional environments. However, there are many diversified research directions in the current literature, such as multi-agent and multi-objective learning, and human-machine interactions. Therefore, in this paper, we propose a comprehensive software architecture that not only plays a vital role in designing a connect-the-dots deep RL architecture but also provides a guideline to develop a realistic RL application in a short time span. By inheriting the proposed architecture, software managers can foresee any challenges when designing a deep RL-based system. As a result, they can expedite the design process and actively control every stage of software development, which is especially critical in agile development environments. For this reason, we design a deep RL-based framework that strictly ensures flexibility, robustness, and scalability. To enforce generalization, the proposed architecture also does not depend on a specific RL algorithm, a network configuration, the number of agents, or the type of agents.

   @article{nguyen2023towards,
     title = {Towards designing a generic and comprehensive deep reinforcement learning framework},
     author = {Nguyen, Ngoc Duy and Nguyen, Thanh Thi and Pham, Nhat Truong and Nguyen, Hai and Nguyen, Dang Tu and Nguyen, Thanh Dang and Lim, Chee Peng and Johnstone, Michael and Bhatti, Asim and Creighton, Douglas and Nahavandi, Saeid},
     journal = {Applied Intelligence},
     volume = {53},
     number = {3},
     pages = {2967--2988},
     year = {2023},
     publisher = {Springer},
     doi = {10.1007/s10489-022-03550-z},
   }