Pinned Repositories
adjustedPSD
bloodbased-pancancer-diagnosis
Benchmarking study of feature extraction methods for cancer diagnosis using blood-based biomarkers. Feature extraction methods are compared both in terms of their performance and robustness
Dermatology-ML
Following repository demonstrates machine learning architectures that can correctly classify lesions between LM and AMH. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which, in conjunction with remote acquisition can expand the range of diagnostic tools in the community. This code is implemented using Keras and Tensorflow frameworks.
dRNASb
Fotomics-Imagification
glioblastoma_exosomal_miR_markers
HybridModel-COVID-19-Prediction
To accurately predict the regional spread of COVID-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia. The results show that the proposed model closely replicates test data. It not only provides accurate predictions but also estimates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model accounting for data limitation. The parameters of the hybrid models were optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict COVID-19 spread under consideration of containment policies, is capable of being used for policy assessment, planning and decision-making.
multiobj_miR_marker_discovery
Multi-objective, network-based microRNA biomarker discovery of complex disease phenotypes
psdMAT
We present a novel pre-processing method (scPSD) inspired by power spectral density analysis to extract important information from large-scale single-cell omics data and enhance the separation of cellular phenotypes.
psdR
Power Spectral Density (psd) preprocessing method in R
VafaeeLab's Repositories
VafaeeLab/dRNASb
VafaeeLab/glioblastoma_exosomal_miR_markers
VafaeeLab/bloodbased-pancancer-diagnosis
Benchmarking study of feature extraction methods for cancer diagnosis using blood-based biomarkers. Feature extraction methods are compared both in terms of their performance and robustness
VafaeeLab/Dermatology-ML
Following repository demonstrates machine learning architectures that can correctly classify lesions between LM and AMH. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which, in conjunction with remote acquisition can expand the range of diagnostic tools in the community. This code is implemented using Keras and Tensorflow frameworks.
VafaeeLab/Fotomics-Imagification
VafaeeLab/HybridModel-COVID-19-Prediction
To accurately predict the regional spread of COVID-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia. The results show that the proposed model closely replicates test data. It not only provides accurate predictions but also estimates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model accounting for data limitation. The parameters of the hybrid models were optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict COVID-19 spread under consideration of containment policies, is capable of being used for policy assessment, planning and decision-making.
VafaeeLab/multiobj_miR_marker_discovery
Multi-objective, network-based microRNA biomarker discovery of complex disease phenotypes
VafaeeLab/psdMAT
We present a novel pre-processing method (scPSD) inspired by power spectral density analysis to extract important information from large-scale single-cell omics data and enhance the separation of cellular phenotypes.
VafaeeLab/psdR
Power Spectral Density (psd) preprocessing method in R
VafaeeLab/adjustedPSD
VafaeeLab/bnsl_ga
Machine learning research project: Bayesian Network Structure Learning using Genetic Algorithms.
VafaeeLab/CFRD_EV_biomarker
Biomarker signature identification for Cystic Fibrosis Related Diabetes from miRNAs in ExtraCellular Vesicles
VafaeeLab/CM-MV-Lipidomics
VafaeeLab/CM_Lipidomics
VafaeeLab/COVID19-TS-Forcast
COVID-19 Time-Series Forcasting
VafaeeLab/COVID_CDR
VafaeeLab/COVIDSpread
VafaeeLab/drugSimDB
Drug Similarity information
VafaeeLab/FeatureSelection_Classification
Benchmarking on Feature selection and classification methods for blood-based biomarker discovery
VafaeeLab/find_website_network
shiny app for FIND network visualization
VafaeeLab/HCC-Recurrence
VafaeeLab/microRNA_marker_discovery
A data-driven, knowledge-based approach to biomarker discovery
VafaeeLab/PanCancer-lncRNAs
VafaeeLab/PCa_proteomics_analysis
VafaeeLab/psdPy
VafaeeLab/SAFAARI
Single-cell Annotation and Fusion with Adversarial Open-Set Domain Adaptation Reliable for Data Integration
VafaeeLab/sample_size_estimation
sample size estimation for ICI responder from microbial data
VafaeeLab/scMulti-omics-data
VafaeeLab/Survival-HCC-Recurrence
Survival analysis study for predicting HCC recurrence one year after surgical resection
VafaeeLab/SVR2019-DL-Models
Repository for the deep learning models I used in my 2019 summer vacation research at UNSW