ORCID Profile
0000-0002-5251-2214
Current Organisations
University of New South Wales
,
University of Technology Sydney
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Publisher: JMIR Publications Inc.
Date: 13-04-2021
DOI: 10.2196/25884
Abstract: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Because rapid automated profiling of peripheral blood s les is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Our work revealed that several clinical parameters that are measurable in blood s les are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores % for disease severity prediction. We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
Publisher: American Chemical Society (ACS)
Date: 23-11-2020
DOI: 10.26434/CHEMRXIV.13271096.V1
Abstract: Repurposing of the existing medications has become the mainstream focus of anti-COVID-19 drug discovery as it offers rapid and cost-effective solutions for therapeutic development. However, there is still a great deal to enhance efficacy of repurposing therapeutic options through combination therapy, in which promising drugs with varying mechanisms of action are administered together. Nonetheless, our ability to identify and validate effective combinations is limited due to the huge number of possible drug pairs. Yet, there is no available resource which can systematically guide to identify or choose the effective in idual drugs or best possible synergistic drug combinations for the treatment of SARS-CoV-2 infection. To address this resource gap, we developed a web-based platform that displays the network-based mechanism of action of drug combinations, thus simultaneously giving a visual of the cellular interactome involved in the mode of action of the chosen drugs. The platform allows the freedom to choose two or more drug combinations and provides the options to investigate network-based efficacy of drug combinations and understand the similarity score, primary indications, and contraindications of using these drugs combinations. In a nutshell, the platform (accessible via: vafaeelab.com/COVID19_repositioning.html) is of the first of its type which provides a systematic approach for pre-clinical investigation of combination therapy for treating COVID-19 on the fingertips of the clinicians or researchers.
Publisher: F1000 Research Ltd
Date: 03-11-2021
DOI: 10.12688/F1000RESEARCH.73969.1
Abstract: A substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with modelling techniques to provide real-time insights. This study introduces a unified platform, COVIDSpread, which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform uses time series models to capture any possible non-linearity in the data. COVIDSpread enables lay users, and experts, to examine the data and develop several customized models with different restrictions such as models developed for a specific time window of the data. COVIDSpread is available here: vafaeelab.com/COVID19TS.html .
Publisher: Oxford University Press (OUP)
Date: 15-04-2021
DOI: 10.1093/BIB/BBAB137
Abstract: Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely ‘PathTurbEr’ (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant s les from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\\beta $ singalling pathways. Next, the aberration in TGF-$\\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC s ling methods, i.e. Neighbourhood s ler (NS) and Hit-and-Run (HAR) s ler that potentially yield robust inference with lower chances of getting stuck at local optima and faster convergence compared to other state-of-art methods. Next, we examined the structural features of the optimal BN as a statistical process that generates the global structure using $p_1$-model, a special class of Exponential Random Graph Models (ERGMs), and MCMC methods for their hyper-parameter s ling. This step enabled key drivers identification that drive the aberration within the perturbed BN structure of STP, and yielded 34, 34 and 23 perturbation driver genes out of 80 constituent genes of three perturbed STP models of TGF-$\\beta $ signalling inferred by NS, HAR and MH s ling methods, respectively. Functional-relevance and disease-relevance analyses suggested their significant associations with breast cancer progression/resistance.
Publisher: MDPI AG
Date: 22-12-2022
DOI: 10.3390/HEALTHCARE11010031
Abstract: Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of in iduals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.
Publisher: Elsevier BV
Date: 09-2021
Publisher: MDPI AG
Date: 22-08-2023
DOI: 10.3390/ELECTRONICS12173541
Abstract: The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion.
Publisher: JMIR Publications Inc.
Date: 20-11-2020
Abstract: ccurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. ecause rapid automated profiling of peripheral blood s les is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. e investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. ur work revealed that several clinical parameters that are measurable in blood s les are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores & % for disease severity prediction. e developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
Publisher: MDPI AG
Date: 25-07-2022
DOI: 10.3390/JPM12081211
Abstract: One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient in iduals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serums les carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood s les, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.COMPBIOMED.2022.105539
Abstract: The brain tumor is one of the deadliest cancerous diseases and its severity has turned it to the leading cause of cancer related mortality. The treatment procedure of the brain tumor depends on the type, location and size of the tumor. Relying solely on human inspection for precise categorization can lead to inevitably dangerous situation. This manual diagnosis process can be improved and accelerated through an automated Computer Aided Diagnosis (CADx) system. In this article, a novel approach using two-stage feature ensemble of deep Convolutional Neural Networks (CNN) is proposed for precise and automatic classification of brain tumors. Three unique Magnetic Resonance Imaging (MRI) datasets and a dataset merging all the unique datasets are considered. The datasets contain three types of brain tumor (meningioma, glioma, pituitary) and normal brain images. From five pre-trained models and a proposed CNN model, the best models are chosen and concatenated in two stages for feature extraction. The best classifier is also chosen among five different classifiers based on accuracy. From the extracted features, most substantial features are selected using Principal Component Analysis (PCA) and fed into the classifier. The robustness of the proposed two stage ensemble model is analyzed using several performance metrics and three different experiments. Through the prominent performance, the proposed model is able to outperform other existing models attaining an average accuracy of 99.13% by optimization of the developed algorithms. Here, the in idual accuracy for Dataset 1, Dataset 2, Dataset 3, and Merged Dataset is 99.67%, 98.16%, 99.76%, and 98.96% respectively. Finally a User Interface (UI) is created using the proposed model for real time validation.
Publisher: MDPI AG
Date: 14-09-2023
Publisher: MDPI AG
Date: 30-03-2023
DOI: 10.3390/S23073604
Abstract: Three-dimensional video services delivered through wireless communication channels have to deal with numerous challenges due to the limitations of both the transmission channel’s bandwidth and receiving devices. Adverse channel conditions, delays, or jitters can result in bit errors and packet losses, which can alter the appearance of stereoscopic 3D (S3D) video. Due to the perception of dissimilar patterns by the two human eyes, they can not be fused into a stable composite pattern in the brain and hence try to dominate by suppressing each other. Thus, a psychovisual sensation that is called binocular rivalry occurs. As a result, undetectable changes causing irritating flickering effects are seen, leading to visual discomforts such as eye strain, headache, nausea, and weariness. This study addresses the observer’s quality of experience (QoE) by analyzing the binocular rivalry impact on the macroblock (MB) losses in a frame and its error propagation due to predictive frame encoding in stereoscopic video transmission systems. To simulate the processing of experimental videos, the Joint Test Model (JM) reference software has been used as it is recommended by the International Telecommunication Union (ITU). Existing error concealing techniques were then applied to the contiguous lost MBs for a variety of transmission impairments. In order to validate the authenticity of the simulated packet loss environment, several objective evaluations were carried out. Standard numbers of subjects were then engaged in the subjective testing of common 3D video sequences. The results were then statistically examined using a standard Student’s t-test, allowing the impact of binocular rivalry to be compared to that of a non-rivalry error condition. The major goal is to assure error-free video communication by minimizing the negative impacts of binocular rivalry and boosting the ability to efficiently integrate 3D video material to improve viewers’ overall QoE.
Publisher: American Chemical Society (ACS)
Date: 12-07-2021
DOI: 10.26434/CHEMRXIV-2021-TK6RT-V2
Abstract: An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the drug primary targets and the SARS-CoV-2–host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with the potential to act synergistically through different, yet potentially complementary pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on in idual drugs or drug pairs. Overall, COVID-CDR is the first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.
Publisher: Elsevier BV
Date: 10-2023
Publisher: American Chemical Society (ACS)
Date: 29-05-2020
DOI: 10.26434/CHEMRXIV.12376505.V1
Abstract: Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an in idual source of evidence, overlooking the wealth and ersity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating erse drug-related information to derive multi-evidenced drug-drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10,367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug-drug similarity network. The resulting database, DrugSimDB currently includes 238,635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (rugSimDB.html) which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information.
Publisher: MDPI AG
Date: 31-05-2021
Abstract: Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD in iduals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.
Publisher: Cambridge University Press (CUP)
Date: 12-07-2021
DOI: 10.33774/CHEMRXIV-2021-TK6RT-V2
Abstract: An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the drug primary targets and the SARS-CoV-2–host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with the potential to act synergistically through different, yet potentially complementary pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on in idual drugs or drug pairs. Overall, COVID-CDR is the first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 18-04-2021
DOI: 10.1101/2021.04.16.21255618
Abstract: Good vaccine safety and reliability are essential to prevent infectious disease spread. A small but significant number of apparent adverse reactions to the new COVID-19 vaccines have been reported. Here, we aim to identify possible common causes for such adverse reactions with a view to enabling strategies that reduce patient risk by using patient data to classify and characterise patients those at risk of such reactions. We examined patient medical histories and data documenting post-vaccination effects and outcomes. The data analyses were conducted by different statistical approaches followed by a set of machine learning classification algorithms. In most cases, similar features were significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, allergic history, taking other medications, type-2 diabetes, hypertension and heart disease are the most significant pre-existing factors associated with risk of poor outcome and long duration of hospital treatments, pyrexia, headache, dyspnoea, chills, fatigue, various kind of pain and dizziness are the most significant clinical predictors. The machine learning classifiers using medical history were also able to predict patients most likely to have complication-free vaccination with an accuracy score above 85%. Our study identifies profiles of in iduals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches. Important classifiers achieving these reactions notably included allergic susceptibility and incidence of heart disease or type-2 diabetes.
No related grants have been discovered for A K M Azad.