VafaeeLab

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/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