By Dr Hamid Alinejad-Rokny

Preprints

Liang Y; Abedini S; Farbehi N; Alinejad-Rokny H, 2024, How chromatin interactions shed light on interpreting non-coding genomic variants: opportunities and future direc-tions, http://dx.doi.org/10.48550/arxiv.2411.17956

Wang F; Lin J; Alinejad-Rokny H; Ma W; Meng L; Huang L; Yu J; Chen N; Wang Y; Yao Z; Xie W; Li X; Wong K-C, 2024, Unveiling multi-scale architectural features in single-cell Hi-C data using scCAFE, http://dx.doi.org/10.1101/2024.09.10.611762

Shamsi A; Becirovic R; Argha A; Abbasnejad E; Alinejad-Rokny H; Mohammadi A, 2024, ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance, http://dx.doi.org/10.48550/arxiv.2409.09251

Doan BG; Shamsi A; Guo X-Y; Mohammadi A; Alinejad-Rokny H; Sejdinovic D; Ranasinghe DC; Abbasnejad E, 2024, Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks, http://dx.doi.org/10.48550/arxiv.2407.20891

Zhu J; Tan M; Yang M; Li R; Alinejad-Rokny H, 2024, CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare, http://dx.doi.org/10.48550/arxiv.2407.19705

Rahmani AM; Khoshvaght P; Alinejad-Rokny H; Sadeghi S; Asghari P; Arabi Z; Hosseinzadeh M, 2024, A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods, http://dx.doi.org/10.48550/arxiv.2406.18568

Rahmani AM; Haider A; Adeli M; Mzoughi O; Gemeay E; Mohammadi M; Alinejad-Rokny H; Khoshvaght P; Hosseinzadeh M, 2024, Enhanced Heart Sound Classification Using Mel Frequency Cepstral Coefficients and Comparative Analysis of Single vs. Ensemble Classifier Strategies, http://dx.doi.org/10.48550/arxiv.2406.00702

Javed S; Khan TM; Qayyum A; Alinejad-Rokny H; Sowmya A; Razzak I, 2024, Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images, http://dx.doi.org/10.48550/arxiv.2405.17520

Truong P; Shen S; Joshi S; Islam MI; Zhong L; Raftery M; Afrasiabi A; Alinejad-Rokny H; Nguyen M; Zou X; Bhuyan GS; Sarowar C; Ghodousi E; Stonehouse O; Mohamed S; Toscan C; Connerty P; Kakadia P; Bohlander S; Michie K; Larsson J; Lock R; Walkley C; Thoms J; Jolly C; Pimanda J, 2024, Genome-Wide CRISPR-Cas9 Screening Identifies a Synergy between Hypomethylating Agents and SUMOylation Blockade in MDS/AML, http://dx.doi.org/10.1101/2024.04.17.589858

Jafari M; Sadeghi D; Shoeibi A; Alinejad-Rokny H; Beheshti A; García DL; Chen Z; Acharya UR; Gorriz JM, 2023, Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023, http://dx.doi.org/10.48550/arxiv.2309.12202

Keramy M; Jahanian K; Sani R; Agha A; Dehzangy I; Yan M; Rokni H, 2023, A survey of machine learning techniques in medical applications, http://dx.doi.org/10.48550/arxiv.2302.13268

Abedini SS; Akhavan S; Heng J; Alizadehsani R; Dehzangi I; Bauer DC; Rokny H, 2023, A Critical Review of the Impact of Candidate Copy Number Variants on Autism Spectrum Disorders, http://dx.doi.org/10.48550/arxiv.2302.03211

Roshanzamir M; Shamsi A; Asgharnezhad H; Alizadehsani R; Hussain S; Moosaei H; Mohammadi A; Acharya UR; Alinejad H, 2023, Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network, http://dx.doi.org/10.20944/preprints202301.0148.v1

Subramanian S; Subramanian S; Thoms JAI; Huang Y; Cornejo P; Koch F; Jacquelin S; Shen S; Song E; Joshi S; Brownlee C; Woll P; Fajardo DC; Beck D; Curtis D; Yehson K; Antonenas V; Brien TO; Trickett A; Powell J; Lewis I; Pitson S; Gandhi M; Lane S; Vafaee F; Wong E; Göttgens B; Rokny HA; Wong JWH; Pimanda J, 2023, Cell Type-Specific Regulation by a Heptad of Transcription Factors in Human Hematopoietic Stem and Progenitor Cells, http://dx.doi.org/10.1101/2023.04.18.537282

Jafari M; Shoeibi A; Khodatars M; Ghassemi N; Moridian P; Delfan N; Alizadehsani R; Khosravi A; Ling SH; Zhang Y-D; Wang S-H; Gorriz JM; Rokny HA; Acharya UR, 2022, Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review, http://dx.doi.org/10.48550/arxiv.2210.14909

Jafari M; Shoeibi A; Ghassemi N; Heras J; Ling SH; Beheshti A; Zhang Y-D; Wang S-H; Alizadehsani R; Gorriz JM; Acharya UR; Rokny HA, 2022, Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence, http://dx.doi.org/10.48550/arxiv.2210.14611

Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Alinejad-Rokny H; Mohammadi A, 2022, HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information, http://dx.doi.org/10.48550/arxiv.2211.02619

Nasab RZ; Ghamsari MRE; Argha A; Macphillamy C; Beheshti A; Alizadehsani R; Lovell NH; Lotfollahi M; Alinejad-Rokny H, 2022, Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View, http://dx.doi.org/10.48550/arxiv.2210.04453

Rahaie Z; Rabiee HR; Alinejad-Rokny H, 2022, DeepGenePrior: A deep learning model to prioritize genes affected by copy number variants, http://dx.doi.org/10.1101/2022.08.22.504862

Parhami P; Fateh M; Rezvani M; Rokny HA, 2022, A benchmarking of deep neural network models for cancer subtyping using single point mutations, http://dx.doi.org/10.1101/2022.07.24.501264

Kazemi A; Hamidieh K; Dashti H; Ghareyazi A; Tahaei MS; Rabiee HR; Alinejad-Rokny H; Dehzangi I, 2022, Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types, http://dx.doi.org/10.21203/rs.3.rs-1567157/v1

Band S; Ardabili S; Yarahmadi A; Pahlevanzadeh B; Kausar Kiani A; Beheshti A; Alinejad Rokny H; Dehzangi I; Chang A; Mosavi A; Moslehpour M, 2022, A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis, http://dx.doi.org/10.20944/preprints202202.0083.v2

Sharifonnasabi F; Jhanjhi N; John J; Obeidy P; Shamshirband S; Alinejad Rokny H; Baz M, 2022, Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography, http://dx.doi.org/10.20944/preprints202108.0413.v3

Band S; Ardabili S; Yarahmadi A; Pahlevanzadeh B; Kausar Kiani A; Beheshti A; Alinejad Rokny H; Dehzangi I; Mosavi A, 2022, Machine Learning and Internet of Medical Things for Handling COVID-19: Meta-Analysis, http://dx.doi.org/10.20944/preprints202202.0083.v1

Sharifonnasabi F; Jhanjhi N; John J; Obeidy P; Shamshirband S; Alinejad Rokny H, 2022, Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography, http://dx.doi.org/10.20944/preprints202108.0413.v2

Rahman MM; Kamal Nasir M; A-Alam N; Islam Khan S; Band S; Dehzangi I; Beheshti A; Alinejad Rokny H, 2022, Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection, http://dx.doi.org/10.20944/preprints202201.0258.v1

Kazemi A; Ghareyazi A; Hamidieh K; Dashti H; Tahaei M; Rabiee H; Alinejad Rokny H; Dehzangi A, 2021, Pan-Cancer Integrative Analysis of Whole-Genome <em>De novo</em> Somatic Point Mutations Reveals 17 Cancer Types, http://dx.doi.org/10.20944/preprints202111.0266.v1

Rezaie N; Bayati M; Tahaei MS; Hamidi M; Khorasani S; Lovell N; Breen J; Rabiee H; Rokny HA, 2021, Somatic point mutations are enriched in long non-coding RNAs with possible regulatory function in breast cancer, http://dx.doi.org/10.21203/rs.3.rs-827525/v1

Islam Khan MS; Rahman A; Karim MR; Bithi NI; Band SS; Dehzangi A; Alinejad-Rokny H, 2021, CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images, http://dx.doi.org/10.1101/2021.05.19.21257430

Debnath T; Reza MM; Rahman A; Band S; Alinejad Rokny H, 2021, Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity, http://dx.doi.org/10.20944/preprints202105.0424.v1

Hamidi H; Alinejad-Rokny H; Coorens T; Sanghvi R; Lindsay SJ; Rahbari R; Ebrahimi D, 2021, Signatures of Mutational Processes in Human DNA Evolution, http://dx.doi.org/10.1101/2021.01.09.426041

Liu N; Low WY; Alinejad-Rokny H; Pederson S; Sadlon T; Barry S; Breen J, 2020, Seeing the forest through the trees: Identifying functional interactions from Hi-C, http://dx.doi.org/10.1101/2020.11.29.402420

Sharifrazi D; Alizadehsani R; Hassannataj Joloudari J; Shamshirband S; Hussain S; Alizadeh Sani Z; Hasanzadeh F; Shoaibi A; Dehzangi A; Alinejad-Rokny H, 2020, CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering, http://dx.doi.org/10.20944/preprints202007.0650.v1

Dashti H; Dehzangi A; Bayati M; Breen J; Lovell N; Ebrahimi D; Rabiee HR; Alinejad-Rokny H, 2020, Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes, http://dx.doi.org/10.1101/2020.05.18.101022

Asgari Y; Heng JIT; Lovell N; Forrest ARR; Alinejad-Rokny H, 2020, Evidence for enhancer noncoding RNAs (enhancer-ncRNAs) with gene regulatory functions relevant to neurodevelopmental disorders, http://dx.doi.org/10.1101/2020.05.16.087395

Alinejad-Rokny H; Modegh RG; Rabiee HR; Rezaie N; Tam KT; Forrest ARR, 2020, MaxHiC: robust estimation of chromatin interaction frequency in Hi-C and capture Hi-C experiments, http://dx.doi.org/10.1101/2020.04.23.056226

Alinejad-Rokny H; Heng JIT; Forrest ARR, 2019, Brain-enriched coding and long non-coding RNA genes are overrepresented in recurrent autism spectrum disorder CNVs, http://dx.doi.org/10.1101/539817

Bayati M; Rabiee HR; Mehrbod M; Vafaee F; Ebrahimi D; Forrest A; Alinejad-Rokny H, 2018, CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes, http://dx.doi.org/10.1101/424960

Alinejad-Rokny H; Zarepour E; Khadijeh Jahanian H; Beheshti A; Dehzangi A, A Multivariate Data Analytics Approach Revealed No Footprint of APOBEC3 Proteins in Hepatitis B Virus Genome, http://dx.doi.org/10.2139/ssrn.3514647

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