By Dr Hamid Alinejad-Rokny

Journal articles

Gooneratne SL; Alinejad-Rokny H; Ebrahimi D; Bohn PS; Wiseman RW; O'Connor DH; Davenport MP; Kent SJ, 2014, 'Linking pig-tailed macaque major histocompatibility complex class I haplotypes and cytotoxic T lymphocyte escape mutations in simian immunodeficiency virus infection', Journal of Virology, 88, pp. 14310 - 14325, http://dx.doi.org/10.1128/JVI.02428-14

Parvin H; Minaei-Bidgoli B; Alinejad-Rokny H, 2013, 'A new imbalanced learning and dictions tree method for breast cancer diagnosis', Journal of Bionanoscience, 7, pp. 673 - 678, http://dx.doi.org/10.1166/jbns.2013.1162

Javanmard R; JeddiSaravi K; Alinejad-Rokny H, 2013, 'Proposed a new method for rules extraction using artificial neural network and artificial immune system in cancer diagnosis', Journal of Bionanoscience, 7, pp. 665 - 672, http://dx.doi.org/10.1166/jbns.2013.1160

Ahmadinia M; Meybodi M; Esnaashari M; Alinejad-Rokny H, 2013, 'Energy-efficient and multi-stage clustering algorithm in wireless sensor networks using cellular learning automata', IETE Journal of Research, 59, pp. 774 - 782, http://dx.doi.org/10.4103/0377-2063.126958

Alinejad-Rokny H; Farzaneh MK; Orimi AG; Pedram MM; Kiasari HA, 2013, 'Proposing a new structure for web mining and personalizing web pages', Journal of Emerging Technologies in Web Intelligence, 5, pp. 287 - 295, http://dx.doi.org/10.4304/jetwi.5.3.287-295

Parvin H; Alinejad-Rokny H; Parvin S, 2013, 'A Classifier Ensemble of Binary Classifier Ensembles', International Journal of Learning Management Systems, 1, pp. 37 - 47, http://dx.doi.org/10.12785/ijlms/010204

Parvin H; Alinejad-Rokny H; Minaei-Bidgoli B; Parvin S, 2013, 'A new classifier ensemble methodology based on subspace learning', Journal of Experimental and Theoretical Artificial Intelligence, 25, pp. 227 - 250, http://dx.doi.org/10.1080/0952813X.2012.715683

Parvin H; Alinejad-Rokny H; Parvin S, 2013, 'A New Clustering Ensemble Framework', International Journal of Learning Management Systems, 1, pp. 19 - 25, http://dx.doi.org/10.12785/ijlms/010103

Parvin H; Minaei-Bidgoli B; Alinejad-Rokny H; Punch WF, 2013, 'Data weighing mechanisms for clustering ensembles', Computers and Electrical Engineering, 39, pp. 1433 - 1450, http://dx.doi.org/10.1016/j.compeleceng.2013.02.004

Parvin H; Alinejad-Rokny H; Seyedaghaee NR; Parvin S, 2012, 'A Heuristic Scalable Classifier Ensemble of Binary Classifier Ensembles', Journal of Bioinformatics and Intelligent Control, 1, pp. 163 - 170, http://dx.doi.org/10.1166/jbic.2013.1016

Sadeghi M; Alinejad-Rokny H, 2012, 'On covering of products of T-generalized state machines', Mathematical Sciences Letters, 1, pp. 43 - 52, http://dx.doi.org/10.12785/msl/010106

Shirvani MH; Alinejad-Rokny H, 2012, 'Performance Assessment of Feasible Scheduling Multiprocessor Tasks Solutions by using DEA FDH method', Information Sciences Letters, 1, pp. 61 - 66, http://dx.doi.org/10.12785/isl/010106

Alizadeh H; Alinejad-Rokny H; Parvin H; Sohrabi B, 2012, 'A new inference engine: Surface Matching Degree', Applied Mathematical Modelling, http://dx.doi.org/10.1016/j.apm.2012.02.027

Esmaeili L; Minaei-Bidgoli B; Alinejad-Rokny H; Nasiri M, 2012, 'Hybrid recommender system for joining virtual communities', Research Journal of Applied Sciences, Engineering and Technology, 4, pp. 500 - 509

Rokny HA; Pedram MM; Shirgahi H, 2011, 'Discovered motifs with using parallel Mprefixspan method', Scientific Research and Essays, 6, pp. 4220 - 4226, http://dx.doi.org/10.5897/sre11.212

Parvin H; Alinejad-Rokny H; Asadi M, 2011, 'An ensemble based approach for feature selection', Australian Journal of Basic and Applied Sciences, 5, pp. 1153 - 1163

Parvin H; Helmi H; Minaei B; Rokny HA; Shirgahi H, 2011, 'Linkage learning based on differences in local optimums of building blocks with one optima', International Journal of Physical Sciences, 6, pp. 3419 - 3425

Minaei-Bidgoli B; Parvin H; Alizadeh H; Alinejad-Rokny H; Punch WF, 2011, 'Effects of resampling method and adaptation on clustering ensemble efficacy', Artificial Intelligence Review, pp. 1 - 22, http://dx.doi.org/10.1007/s10462-011-9295-x

Conference Papers

Argha A; Li J; Magdy J; Alinejad-Rokny H; Celler BG; Butcher K; Ooi SY; Lovell NH, 2023, 'Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm', in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, http://dx.doi.org/10.1109/EMBC40787.2023.10341108

Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Alinejad-Rokny H; Mohammadi A, 2023, 'HYDRA-HGR: A Hybrid Transformer-Based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information', in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, http://dx.doi.org/10.1109/ICASSP49357.2023.10096192

Asgharnezhad H; Shamsi A; Bakhshayeshi I; Alizadehsani R; Chamaani S; Alinejad-Rokny H, 2023, 'Improving PPG Signal Classification with Machine Learning: The Power of a Second Opinion', in International Conference on Digital Signal Processing, DSP, http://dx.doi.org/10.1109/DSP58604.2023.10167869

Shahabikargar M; Beheshti A; Khatami A; Nguyen R; Zhang X; Alinejad-Rokny H, 2022, 'Domain Knowledge Enhanced Text Mining for Identifying Mental Disorder Patterns', in Proceedings - 2022 IEEE 9th International Conference on Data Science and Advanced Analytics, DSAA 2022, http://dx.doi.org/10.1109/DSAA54385.2022.10032346

Khozeimeh F; Roshanzamir M; Shoeibi A; Darbandy MT; Alizadehsani R; Alinejad-Rokny H; Ahmadian D; Khosravi A; Nahavandi S, 2022, 'Importance of Wearable Health Monitoring Systems Using IoMT; Requirements, Advantages, Disadvantages and Challenges', in IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics and 8th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, CINTI-MACRo 2022 - Proceedings, pp. 245 - 250, http://dx.doi.org/10.1109/CINTI-MACRo57952.2022.10029528

Danaei S; Bostani A; Moravvej SV; Mohammadi F; Alizadehsani R; Shoeibi A; Alinejad-Rokny H; Nahavandi S, 2022, 'Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning', in IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics and 8th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, CINTI-MACRo 2022 - Proceedings, pp. 265 - 270, http://dx.doi.org/10.1109/CINTI-MACRo57952.2022.10029403

Conference Abstracts

Truong P; Shen S; Joshi S; Afrasiabi A; Zhong L; Raftery MJ; Larsson J; Lock RB; Walkley CR; Rokny HA; Thoms JAI; Jolly CJ; Pimanda JE, 2022, 'Genome-Wide CRISPR-Cas9 Screening Identifies a Synergy between Hypomethylating Agents and Sumoylation Blockade in Myelodysplastic Syndromes and Acute Myeloid Leukemia', in BLOOD, AMER SOC HEMATOLOGY, LA, New Orleans, Vol. 140, presented at 64th Annual Meeting and Exposition of the American-Society-of-Hematology (ASH), LA, New Orleans, 10 December 2022 - 13 December 2022, http://dx.doi.org/10.1182/blood-2022-165713

Gooneratne S; Alinejad-Rokny H; Mohammadi D; Bohn P; Wiseman R; O'Connor D; Davenport M; Kent S, 2015, 'LINKING PIGTAIL MACAQUE MHC I HAPLOTYPES AND CTL ESCAPE MUTATIONS IN SIV INFECTION', in JOURNAL OF MEDICAL PRIMATOLOGY, WILEY-BLACKWELL, Vol. 44, pp. 335 - 335, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000361966000094&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

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

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