Trần Trần - Academia.edu (original) (raw)

Papers by Trần Trần

Social Science Research Network, 2022

PeerJ, May 11, 2022

The exploration of drug-target interactions (DTI) is an essential stage in the drug development p... more The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.

Advances in Systems Analysis, Software Engineering, and High Performance Computing, 2020

Applying deep learning to the pervasive graph data is significant because of the unique character... more Applying deep learning to the pervasive graph data is significant because of the unique characteristics of graphs. Recently, substantial amounts of research efforts have been keen on this area, greatly advancing graph-analyzing techniques. In this study, the authors comprehensively review different kinds of deep learning methods applied to graphs. They discuss with existing literature into sub-components of two: graph convolutional networks, graph autoencoders, and recent trends including chemoinformatics research area including molecular fingerprints and drug discovery. They further experiment with variational autoencoder (VAE) analyze how these apply in drug target interaction (DTI) and applications with ephemeral outline on how they assist the drug discovery pipeline and discuss potential research directions.

Journal of Hospital Management and Health Policy, 2021

Precision medicine aims to integrate an individual's unique features from clinical phenotypes and... more Precision medicine aims to integrate an individual's unique features from clinical phenotypes and biological information obtained from imaging to laboratory tests and health records, to arrive at a tailored diagnostic or therapeutic solution. The premise that precision medicine will reduce disease-related health and financial burden is theoretically sound, but its realisation in clinical practice is still nascent. In contrast to conventional medicine, developing precision medicine solutions is highly data-intensive and to accelerate this effort there are initiatives to collect vast amounts of clinical and biomedical data. Over the last decade, artificial intelligence (AI), which includes machine learning (ML), has demonstrated unparalleled success in pattern recognition from big data in a range of domains from shopping recommendation to image classification. It is not surprising that ML is being considered as the critical technology that can transform big data from biobanks and electronic health records (EHRs) into clinically applicable precision medicine tools at the bedside. Distillation of high-dimensional data across clinical, biological, patient-generated and environmental domains using ML and translating garnered insights into clinical practice requires not only extant algorithms but also additional development of newer methods and tools. In this review, we provide a broad overview of the prospects and potential for AI in precision medicine and discuss some of the challenges and evolving solutions that are revolutionising healthcare.

Journal of Cellular Physiology, 2019

Intracellular Ca2+ signals are essential for stem cell function and play a significant role in th... more Intracellular Ca2+ signals are essential for stem cell function and play a significant role in the differentiation process. Dental pulp stem cells (DPSCs) are a potential source of stem cells; however, the mechanisms controlling cell differentiation remain largely unknown. Utilizing rat DPSCs, we examined the effect of adenosine triphosphate (ATP) on osteoblast differentiation and characterized its mechanism of action using real‐time Ca 2+ imaging analysis. Our results revealed that ATP enhanced osteogenesis as indicated by Ca 2+ deposition in the extracellular matrix via Alizarin Red S staining. This was consistent with upregulation of osteoblast genes BMP2, Mmp13, Col3a1, Ctsk, Flt1, and Bgn. Stimulation of DPSCs with ATP (1–300 µM) increased intracellular Ca 2+ signals in a concentration‐dependent manner, whereas histamine, acetylcholine, arginine vasopressin, carbachol, and stromal‐cell‐derived factor‐1α failed to do so. Depletion of intracellular Ca 2+ stores in the endoplasmic...

PloS one, 2015

Exercise offers short-term and long-term health benefits, including an increased metabolic rate a... more Exercise offers short-term and long-term health benefits, including an increased metabolic rate and energy expenditure in myocardium. The newly-discovered exercise-induced myokine, irisin, stimulates conversion of white into brown adipocytes as well as increased mitochondrial biogenesis and energy expenditure. Remarkably, irisin is highly expressed in myocardium, but its physiological effects in the heart are unknown. The objective of this work is to investigate irisin's potential multifaceted effects on cardiomyoblasts and myocardium. For this purpose, H9C2 cells were treated with recombinant irisin produced in yeast cells (r-irisin) and in HEK293 cells (hr-irisin) for examining its effects on cell proliferation by MTT [3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay and on gene transcription profiles by qRT-PCR. R-irisin and hr-irisin both inhibited cell proliferation and activated genes related to cardiomyocyte metabolic function and differentiation, incl...

Molecular and cellular endocrinology, Jan 16, 2015

Intracellular Ca(2+) signaling is important for stem cell differentiation and there is evidence i... more Intracellular Ca(2+) signaling is important for stem cell differentiation and there is evidence it may coordinate the process. Arginine vasopressin (AVP) is a neuropeptide hormone secreted mostly from the posterior pituitary gland and increases Ca(2+) signals mainly via V1 receptors. However, the role of AVP in adipogenesis of human adipose-derived stem cells (hASCs) is unknown. In this study, we identified the V1a receptor gene in hASCs and demonstrated that AVP stimulation increased intracellular Ca(2+) concentration during adipogenesis. This effect was mediated via V1a receptors, Gq-proteins and the PLC-IP3 pathway. These Ca(2+) signals were due to endoplasmic reticulum release and influx from the extracellular space. Furthermore, AVP supplementation to the adipogenic medium decreased the number of adipocytes and adipocyte marker genes during differentiation. The effect of AVP on adipocyte formation was reversed by the V1a receptor blocker V2255. These findings suggested that AVP...

Biochemical Journal, 2014

Intracellular Ca2+ oscillations are frequently observed during stem cell differentiation, and the... more Intracellular Ca2+ oscillations are frequently observed during stem cell differentiation, and there is evidence that it may control adipogenesis. The transient receptor potential melastatin 4 channel (TRPM4) is a key regulator of Ca2+ signals in excitable and non-excitable cells. However, its role in human adipose-derived stem cells (hASCs), in particular during adipogenesis, is unknown. We have investigated TRPM4 in hASCs and examined its impact on histamine-induced Ca2+ signalling and adipogenesis. Using reverse transcription (RT)–PCR, we have identified TRPM4 gene expression in hASCs and human adipose tissue. Electrophysiological recordings revealed currents with the characteristics of those reported for the channel. Furthermore, molecular suppression of TRPM4 with shRNA diminished the Ca2+ signals generated by histamine stimulation, mainly via histamine receptor 1 (H1) receptors. The increases in intracellular Ca2+ were due to influx via voltage-dependent Ca2+ channels (VDCCs) o...

IEEE Signal Processing Letters, 2002

This letter describes an algorithm for systematically finding a multiplierless approximation of t... more This letter describes an algorithm for systematically finding a multiplierless approximation of transforms by replacing floating-point multipliers with VLSI-friendly binary coefficients of the form 2. Assuming the cost of hardware binary shifters is negligible, the total number of binary adders employed to approximate the transform can be regarded as an index of complexity. Because the new algorithm is more systematic and faster than trial-and-error binary approximations with adder constraint, it is a much more efficient design tool. Furthermore, the algorithm is not limited to a specific transform; various approximations of the discrete cosine transform are presented as examples of its versatility.