Binh P. Nguyen
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View article: Editorial: Enhancing drug discovery through structure-based design and computational techniques
Editorial: Enhancing drug discovery through structure-based design and computational techniques Open
View article: Estimating Nutritional Composition from Food Volume Via Deep Learning-Based Depth and Segmentation Models
Estimating Nutritional Composition from Food Volume Via Deep Learning-Based Depth and Segmentation Models Open
Nutrition plays a critical role in human health, with a balanced diet being essential for preventing non-communicable diseases, enhancing immune function, and improving quality of life. However, dietary imbalances contribute to significant…
View article: AIM: an accurate and explainable model for ATAC to GEX translation and pathway analysis
AIM: an accurate and explainable model for ATAC to GEX translation and pathway analysis Open
The development of multimodal technologies has enabled the simultaneous measurement of various cellular modalities, such as chromatin accessibility (ATAC), gene expression (GEX), and surface protein abundance in single cells. However, the …
View article: CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data
CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data Open
Class imbalance, where datasets often lack sufficient samples for minority classes, is a persistent challenge in machine learning. Existing solutions often generate synthetic data to mitigate this issue, but they typically struggle with co…
View article: Addressing imbalance in health data: Synthetic minority oversampling using deep learning
Addressing imbalance in health data: Synthetic minority oversampling using deep learning Open
Class imbalances in healthcare data, characterized by a disproportionate number of positive cases compared to negative ones, can lead to biased machine learning models that favor the majority class. Ensuring good performance across all cla…
View article: Deterministic Autoencoder using Wasserstein loss for tabular data generation
Deterministic Autoencoder using Wasserstein loss for tabular data generation Open
Tabular data generation is a complex task due to its distinctive characteristics and inherent complexities. While Variational Autoencoders have been adapted from the computer vision domain for tabular data synthesis, their reliance on non-…
View article: TTVAE: Transformer-based generative modeling for tabular data generation
TTVAE: Transformer-based generative modeling for tabular data generation Open
View article: Distributional Surgery for Language Model Activations
Distributional Surgery for Language Model Activations Open
View article: Blending is all you need: Data-centric ensemble synthetic data
Blending is all you need: Data-centric ensemble synthetic data Open
View article: 1438 EMERALD-Y90: phase 2 study of transarterial radioembolization followed by durvalumab and bevacizumab for treatment of participants with unresectable hepatocellular carcinoma eligible for embolization
1438 EMERALD-Y90: phase 2 study of transarterial radioembolization followed by durvalumab and bevacizumab for treatment of participants with unresectable hepatocellular carcinoma eligible for embolization Open
View article: Challenges and opportunities of generative models on tabular data
Challenges and opportunities of generative models on tabular data Open
View article: Structural Basis for Dimerization and Activation of UvrD-family Helicases
Structural Basis for Dimerization and Activation of UvrD-family Helicases Open
UvrD-family helicases are superfamily 1A motor proteins that function during DNA replication, recombination, repair, and transcription. UvrD family monomers translocate along single stranded (ss) DNA but need to be activated by dimerizatio…
View article: Effectiveness of COVID-19 vaccines against hospitalisation, death and infection over time in Aotearoa New Zealand: a retrospective cohort study
Effectiveness of COVID-19 vaccines against hospitalisation, death and infection over time in Aotearoa New Zealand: a retrospective cohort study Open
aims: This study aimed to evaluate the effectiveness of COVID-19 vaccines in preventing COVID-19 outcomes when the Omicron variant was predominant in Aotearoa New Zealand. methods: We conducted a retrospective cohort study using routinely …
View article: ResGAT: Residual Graph Attention Networks for molecular property prediction
ResGAT: Residual Graph Attention Networks for molecular property prediction Open
Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no…
View article: Editorial: Prediction of protein-protein interactions (PPIs): the next frontier
Editorial: Prediction of protein-protein interactions (PPIs): the next frontier Open
Keywords: protein-protein interaction (PPI), protein-protein docking, homolog modeling, drug design, structural biology, molecular recognition
View article: Adaptive edge prior-based deep attention residual network for low-dose CT image denoising
Adaptive edge prior-based deep attention residual network for low-dose CT image denoising Open
View article: Molecular representations in bio-cheminformatics
Molecular representations in bio-cheminformatics Open
Molecular representations have essential roles in bio-cheminformatics as they facilitate the growth of machine learning applications in numerous sub-domains of biology and chemistry, especially drug discovery. These representations transfo…
View article: A Contrastive Learning and Graph-based Approach for Missing Modalities in Multimodal Federated Learning
A Contrastive Learning and Graph-based Approach for Missing Modalities in Multimodal Federated Learning Open
Federated Learning has emerged as a decentralized method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while keeping their data st…
View article: Hierarchical Federated Learning in MEC Networks with Knowledge Distillation
Hierarchical Federated Learning in MEC Networks with Knowledge Distillation Open
Modern automobiles are equipped with advanced computing capabilities, allowing them to become powerful computing units capable of processing a large amount of data and training machine learning models. However, machine learning algorithms …
View article: Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population Dynamics
Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population Dynamics Open
Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with do…
View article: An efficient hybrid deep learning architecture for predicting short antimicrobial peptides
An efficient hybrid deep learning architecture for predicting short antimicrobial peptides Open
Short‐length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing …
View article: The Importance of Corporate Responsibility in Organizational Behavior
The Importance of Corporate Responsibility in Organizational Behavior Open
View article: Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint
Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint Open
The development of technological devices, such as satellites and drones, has made it easier to collect images and videos from the air. From these vast data sources, the problem of detecting objects in aerial images is formed to serve situa…
View article: The Importance of Corporate Responsibility in Organizational Behavior
The Importance of Corporate Responsibility in Organizational Behavior Open
View article: Enhancing public research on citizen data: An empirical investigation of data synthesis using Statistics New Zealand’s Integrated Data Infrastructure
Enhancing public research on citizen data: An empirical investigation of data synthesis using Statistics New Zealand’s Integrated Data Infrastructure Open
The Integrated Data Infrastructure (IDI) in New Zealand is a critical asset that integrates citizen data from various public and private organizations for population-level analyses. However, access restrictions within the IDI environment p…
View article: Synthetic minority oversampling using edited displacement-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si42.svg" display="inline" id="d1e3258"> <mml:mi>k</mml:mi> </mml:math> -nearest neighbors
Synthetic minority oversampling using edited displacement-based -nearest neighbors Open
Skewed class proportions in real-world datasets present a challenge for machine learning algorithms, as they have a tendency to correctly categorize the majority class while incorrectly classifying the minority class. Such classification d…
View article: Contributions of individual satellite cells to muscle regeneration assessed using a confetti mouse model
Contributions of individual satellite cells to muscle regeneration assessed using a confetti mouse model Open
Summary Insufficient regeneration is implicated in muscle pathologies, but much remains unknown about the regenerative output of individual muscle stem cells, called satellite cells (SCs). Prior work showed that individual SCs contribute t…
View article: Herpes zoster vaccine safety in the Aotearoa New Zealand population: a self-controlled case series study
Herpes zoster vaccine safety in the Aotearoa New Zealand population: a self-controlled case series study Open
View article: Exploring Graph-based Transformer Encoder for Low-Resource Neural Machine Translation
Exploring Graph-based Transformer Encoder for Low-Resource Neural Machine Translation Open
The Transformer is commonly used in Neural Machine Translation (NMT), but it faces issues with over-parameterization in low-resource settings. This means that simply increasing the model parameters significantly will not lead to improved p…
View article: Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game
Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game Open
Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physio…