By Dr Hamid Alinejad-Rokny

Journal articles

Lee SW; Tanveer J; Rahmani AM; Alinejad-Rokny H; Khoshvaght P; Zare G; Malekpour Alamdari P; Hosseinzadeh M, 2025, 'SFGCN: Synergetic fusion-based graph convolutional networks approach for link prediction in social networks', Information Fusion, 114, http://dx.doi.org/10.1016/j.inffus.2024.102684

Zobeiri A; Rezaee A; Hajati F; Argha A; Alinejad-Rokny H, 2025, 'Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis', International Journal of Medical Informatics, 193, http://dx.doi.org/10.1016/j.ijmedinf.2024.105659

Ahmadi-Dastgerdi N; Hosseini-Nejad H; Alinejad-Rokny H, 2024, 'A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems', International Journal of Neural Systems, 34, http://dx.doi.org/10.1142/S0129065724500679

Rahaie Z; Rabiee HR; Alinejad-Rokny H, 2024, 'CNVDeep: deep association of copy number variants with neurocognitive disorders', BMC Bioinformatics, 25, http://dx.doi.org/10.1186/s12859-024-05874-8

Truong P; Shen S; Joshi S; Islam MI; Zhong L; Raftery MJ; Afrasiabi A; Alinejad-Rokny H; Nguyen M; Zou X; Bhuyan GS; Sarowar CH; Ghodousi ES; Stonehouse O; Mohamed S; Toscan CE; Connerty P; Kakadia PM; Bohlander SK; Michie KA; Larsson J; Lock RB; Walkley CR; Thoms JAI; Jolly CJ; Pimanda JE, 2024, 'TOPORS E3 ligase mediates resistance to hypomethylating agent cytotoxicity in acute myeloid leukemia cells', Nature Communications, 15, pp. 7360, http://dx.doi.org/10.1038/s41467-024-51646-6

Sadeghi A; Hajati F; Rezaee A; Sadeghi M; Argha A; Alinejad-Rokny H, 2024, '3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection', Computers in Biology and Medicine, 182, http://dx.doi.org/10.1016/j.compbiomed.2024.109126

Abedini SS; Akhavantabasi S; Liang Y; Heng JIT; Alizadehsani R; Dehzangi I; Bauer DC; Alinejad-Rokny H, 2024, 'A critical review of the impact of candidate copy number variants on autism spectrum disorder', Mutation Research - Reviews in Mutation Research, 794, http://dx.doi.org/10.1016/j.mrrev.2024.108509

Xue J; Alinejad-Rokny H; Liang K, 2024, 'Navigating micro- and nano-motors/swimmers with machine learning: Challenges and future directions', ChemPhysMater, 3, pp. 273 - 283, http://dx.doi.org/10.1016/j.chphma.2024.06.001

Zahedi R; Ghamsari R; Argha A; Macphillamy C; Beheshti A; Alizadehsani R; Lovell NH; Lotfollahi M; Alinejad-Rokny H, 2024, 'Deep learning in spatially resolved transcriptfomics: A comprehensive technical view', Briefings in Bioinformatics, 25, http://dx.doi.org/10.1093/bib/bbae082

Razzak I; Naz S; Alinejad-Rokny H; Nguyen TN; Khalifa F, 2024, 'A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs', IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21, pp. 573 - 581, http://dx.doi.org/10.1109/TCBB.2022.3219032

Argha A; Alinejad-Rokny H; Baumgartner M; Schreier G; Celler BG; Redmond SJ; Butcher K; Ooi SY; Lovell NH, 2024, 'A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms', IEEE Transactions on Biomedical Engineering, http://dx.doi.org/10.1109/TBME.2024.3476088

MacPhillamy C; Chen T; Hiendleder S; Williams JL; Alinejad-Rokny H; Low WY, 2024, 'DNA methylation analysis to differentiate reference, breed, and parent-of-origin effects in the bovine pangenome era', GigaScience, 13, http://dx.doi.org/10.1093/gigascience/giae061

Jafari M; Sadeghi D; Shoeibi A; Alinejad-Rokny H; Beheshti A; García DL; Chen Z; Acharya UR; Gorriz JM, 2024, 'Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023', Applied Intelligence, 54, pp. 35 - 79, http://dx.doi.org/10.1007/s10489-023-05155-6

Islam S; Mugdha SBS; Dipta SR; Arafat ME; Shatabda S; Alinejad-Rokny H; Dehzangi I, 2024, 'MethEvo: an accurate evolutionary information-based methylation site predictor', Neural Computing and Applications, 36, pp. 201 - 212, http://dx.doi.org/10.1007/s00521-022-07738-9

Taylor R; Taylor J; Denisenko E; Jones M; Clayton J; Laing N; Forrest A; Alinejad-Rokny H; Ravenscroft G, 2024, '278P Mapping human skeletal muscle enhancers to increase rates of genetic diagnosis', Neuromuscular Disorders, 43, pp. 104441.87 - 104441.87, http://dx.doi.org/10.1016/j.nmd.2024.07.096

Hansun S; Argha A; Alinejad-Rokny H; Alizadehsani R; Gorriz JM; Liaw S-T; Celler BG; Marks GB, 2024, 'A New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection', IEEE Transactions on Radiation and Plasma Medical Sciences, pp. 1 - 1, http://dx.doi.org/10.1109/trpms.2024.3474708

Wang F; Alinejad-Rokny H; Lin J; Gao T; Chen X; Zheng Z; Meng L; Li X; Wong KC, 2023, 'A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level', Advanced Science, 10, http://dx.doi.org/10.1002/advs.202303502

Subramanian S; Thoms JAI; Huang Y; Cornejo-Páramo P; Koch FC; Jacquelin S; Shen S; Song E; Joshi S; Brownlee C; Woll PS; Chacon-Fajardo D; Beck D; Curtis DJ; Yehson K; Antonenas V; O'Brien T; Trickett A; Powell JA; Lewis ID; Pitson SM; Gandhi MK; Lane SW; Vafaee F; Wong ES; Göttgens B; Alinejad-Rokny H; Wong JWH; Pimanda JE, 2023, 'Genome-wide transcription factor–binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs', Blood, 142, pp. 1448 - 1462, http://dx.doi.org/10.1182/blood.2023021120

Shamsi A; Asgharnezhad H; Bouchani Z; Jahanian K; Saberi M; Wang X; Razzak I; Alizadehsani R; Mohammadi A; Alinejad-Rokny H, 2023, 'A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis', Neural Computing and Applications, 35, pp. 22179 - 22188, http://dx.doi.org/10.1007/s00521-023-08930-1

Parhami P; Fateh M; Rezvani M; Alinejad-Rokny H, 2023, 'A comparison of deep neural network models for cluster cancer patients through somatic point mutations', Journal of Ambient Intelligence and Humanized Computing, 14, pp. 10883 - 10898, http://dx.doi.org/10.1007/s12652-022-04351-5

Rahaie Z; Rabiee HR; Alinejad-Rokny H, 2023, 'DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants', PLoS Computational Biology, 19, http://dx.doi.org/10.1371/journal.pcbi.1011249

Ghamsari R; Rosenbluh J; Menon AV; Lovell NH; Alinejad-Rokny H, 2023, 'Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers', Cancers, 15, http://dx.doi.org/10.3390/cancers15143566

Jafari M; Shoeibi A; Khodatars M; Ghassemi N; Moridian P; Alizadehsani R; Khosravi A; Ling SH; Delfan N; Zhang YD; Wang SH; Gorriz JM; Alinejad-Rokny H; Acharya UR, 2023, 'Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review', Computers in Biology and Medicine, 160, http://dx.doi.org/10.1016/j.compbiomed.2023.106998

Khozeimeh F; Alizadehsani R; Shirani M; Tartibi M; Shoeibi A; Alinejad-Rokny H; Harlapur C; Sultanzadeh SJ; Khosravi A; Nahavandi S; Tan RS; Acharya UR, 2023, 'ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease', Computers in Biology and Medicine, 158, http://dx.doi.org/10.1016/j.compbiomed.2023.106841

Shoeibi A; Khodatars M; Jafari M; Ghassemi N; Moridian P; Alizadehsani R; Ling SH; Khosravi A; Alinejad-Rokny H; Lam HK; Fuller-Tyszkiewicz M; Acharya UR; Anderson D; Zhang Y; Gorriz JM, 2023, 'Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review', Information Fusion, 93, pp. 85 - 117, http://dx.doi.org/10.1016/j.inffus.2022.12.010

Shabani N; Beheshti A; Farhood H; Bower M; Garrett M; Alinejad-Rokny H, 2023, 'A Rule-Based Approach for Mining Creative Thinking Patterns from Big Educational Data', AppliedMath, 3, pp. 243 - 267, http://dx.doi.org/10.3390/appliedmath3010014

Wang S; Beheshti A; Wang Y; Lu J; Sheng QZ; Elbourn S; Alinejad-Rokny H, 2023, 'Learning Distributed Representations and Deep Embedded Clustering of Texts', Algorithms, 16, http://dx.doi.org/10.3390/a16030158

Labani M; Beheshti A; Argha A; Alinejad-Rokny H, 2023, 'A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants', International Journal of Molecular Sciences, 24, http://dx.doi.org/10.3390/ijms24032472

Alizadehsani R; Roshanzamir M; Izadi NH; Gravina R; Kabir HMD; Nahavandi D; Alinejad-Rokny H; Khosravi A; Acharya UR; Nahavandi S; Fortino G, 2023, 'Swarm Intelligence in Internet of Medical Things: A Review', Sensors, 23, http://dx.doi.org/10.3390/s23031466

Azim SM; Sabab NHN; Noshadi I; Alinejad-Rokny H; Sharma A; Shatabda S; Dehzangi I, 2023, 'Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers', Informatics in Medicine Unlocked, 42, http://dx.doi.org/10.1016/j.imu.2023.101348

Hong L; Modirrousta MH; Hossein Nasirpour M; Mirshekari Chargari M; Mohammadi F; Moravvej SV; Rezvanishad L; Rezvanishad M; Bakhshayeshi I; Alizadehsani R; Razzak I; Alinejad-Rokny H; Nahavandi S, 2023, 'GAN-LSTM-3D: An efficient method for lung tumour 3D reconstruction enhanced by attention-based LSTM', CAAI Transactions on Intelligence Technology, http://dx.doi.org/10.1049/cit2.12223

Hansun S; Argha A; Alinejad-Rokny H; Liaw ST; Celler BG; Marks GB, 2023, 'Revisiting Transfer Learning Method for Tuberculosis Diagnosis', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, http://dx.doi.org/10.1109/EMBC40787.2023.10340441

Grapotte M; Saraswat M; Bessière C; Menichelli C; Ramilowski JA; Severin J; Hayashizaki Y; Itoh M; Tagami M; Murata M; Kojima-Ishiyama M; Noma S; Noguchi S; Kasukawa T; Hasegawa A; Suzuki H; Nishiyori-Sueki H; Frith MC; Abugessaisa I; Aitken S; Aken BL; Alam I; Alam T; Alasiri R; Alhendi AMN; Alinejad-Rokny H; Alvarez MJ; Andersson R; Arakawa T; Araki M; Arbel T; Archer J; Archibald AL; Arner E; Arner P; Asai K; Ashoor H; Astrom G; Babina M; Baillie JK; Bajic VB; Bajpai A; Baker S; Baldarelli RM; Balic A; Bansal M; Batagov AO; Batzoglou S; Beckhouse AG; Beltrami AP; Beltrami CA; Bertin N; Bhattacharya S; Bickel PJ; Blake JA; Blanchette M; Bodega B; Bonetti A; Bono H; Bornholdt J; Bttcher M; Bougouffa S; Boyd M; Breda J; Brombacher F; Brown JB; Bult CJ; Burroughs AM; Burt DW; Busch A; Caglio G; Califano A; Cameron CJ; Cannistraci CV; Carbone A; Carlisle AJ; Carninci P; Carter KW; Cesselli D; Chang JC; Chen JC; Chen Y; Chierici M; Christodoulou J; Ciani Y; Clark EL; Coskun M; Dalby M; Dalla E; Daub CO; Davis CA; de Hoon MJL; de Rie D; Denisenko E; Deplancke B; Detmar M; Deviatiiarov R; Di Bernardo D; Diehl AD; Dieterich LC, 2022, 'Author Correction: Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network (Nature Communications, (2021), 12, 1, (3297), 10.1038/s41467-021-23143-7)', Nature Communications, 13, http://dx.doi.org/10.1038/s41467-022-28758-y

Debnath T; Reza MM; Rahman A; Beheshti A; Band SS; Alinejad-Rokny H, 2022, 'Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity', Scientific Reports, 12, http://dx.doi.org/10.1038/s41598-022-11173-0

Dashti H; Dehzangi I; Bayati M; Breen J; Beheshti A; Lovell N; Rabiee HR; Alinejad-Rokny H, 2022, 'Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer', BMC Bioinformatics, 23, http://dx.doi.org/10.1186/s12859-022-04652-8

Ghareyazi A; Kazemi A; Hamidieh K; Dashti H; 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', BMC Bioinformatics, 23, http://dx.doi.org/10.1186/s12859-022-04840-6

Rezaie N; Bayati M; Hamidi M; Tahaei MS; Khorasani S; Lovell NH; Breen J; Rabiee HR; Alinejad-Rokny H, 2022, 'Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer', Communications Biology, 5, http://dx.doi.org/10.1038/s42003-022-03528-0

Afrasiabi A; Alinejad-Rokny H; Khosh A; Rahnama M; Lovell N; Xu Z; Ebrahimi D, 2022, 'The low abundance of CpG in the SARS-CoV-2 genome is not an evolutionarily signature of ZAP', Scientific Reports, 12, pp. 2420, http://dx.doi.org/10.1038/s41598-022-06046-5

Subramanian S; Thoms JA; Huang Y; Jacquelin S; Shen S; Song E; Joshi S; Brownlee C; Woll PS; Fajardo DC; Beck D; Curtis DJ; Yehson K; Antonenas V; O' Brien T; Trickett A; Powell J; Pitson SM; Gandhi MK; Cornejo P; Wong E; Lane SW; Gottgens B; Rokny HA; Wong JWH; Pimanda JE, 2022, 'Comparative Analysis of Genome-Scale Gene Regulatory Networks in Human Hematopoietic Stem and Myeloid Progenitor Fractions', BLOOD, 140, pp. 2846 - 2848, http://dx.doi.org/10.1182/blood-2022-165620

Labani M; Beheshti A; Lovell NH; Alinejad-Rokny H; Afrasiabi A, 2022, 'KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition', International Journal of Molecular Sciences, 23, http://dx.doi.org/10.3390/ijms232214418

MacPhillamy C; Alinejad-Rokny H; Pitchford WS; Low WY, 2022, 'Cross-species enhancer prediction using machine learning', Genomics, 114, http://dx.doi.org/10.1016/j.ygeno.2022.110454

Saberi-Movahed F; Mohammadifard M; Mehrpooya A; Rezaei-Ravari M; Berahmand K; Rostami M; Karami S; Najafzadeh M; Hajinezhad D; Jamshidi M; Abedi F; Mohammadifard M; Farbod E; Safavi F; Dorvash M; Mottaghi-Dastjerdi N; Vahedi S; Eftekhari M; Saberi-Movahed F; Alinejad-Rokny H; Band SS; Tavassoly I, 2022, 'Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods', Computers in Biology and Medicine, 146, http://dx.doi.org/10.1016/j.compbiomed.2022.105426

Band SS; Ardabili S; Yarahmadi A; Pahlevanzadeh B; Kiani AK; 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', Frontiers in Public Health, 10, http://dx.doi.org/10.3389/fpubh.2022.869238

Alinejad-Rokny H; Modegh RG; Rabiee HR; Sarbandi ER; Rezaie N; Tam KT; Forrest ARR, 2022, 'MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments', PLoS Computational Biology, 18, http://dx.doi.org/10.1371/journal.pcbi.1010241

Sharifonnasabi F; Jhanjhi NZ; John J; Obeidy P; Band SS; Alinejad-Rokny H; Baz M, 2022, 'Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography', Frontiers in Public Health, 10, http://dx.doi.org/10.3389/fpubh.2022.879418

Afrasiabi A; Keane JT; Ong LTC; Alinejad-Rokny H; Fewings NL; Booth DR; Parnell GP; Swaminathan S, 2022, 'Genetic and transcriptomic analyses support a switch to lytic phase in Epstein Barr virus infection as an important driver in developing Systemic Lupus Erythematosus', Journal of Autoimmunity, 127, http://dx.doi.org/10.1016/j.jaut.2021.102781

Argha A; Celler BG; Alinejad-Rokny H; Lovell NH, 2022, 'Blood Pressure Estimation From Korotkoff Sound Signals Using an End-to-End Deep-Learning-Based Algorithm', IEEE Transactions on Instrumentation and Measurement, 71, http://dx.doi.org/10.1109/TIM.2022.3217865

Sharifrazi D; Alizadehsani R; Joloudari JH; Band SS; Hussain S; Sani ZA; Hasanzadeh F; Shoeibi A; Dehzangi A; Sookhak M; Alinejad-Rokny H, 2022, 'CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering', Mathematical Biosciences and Engineering, 19, pp. 2381 - 2402, http://dx.doi.org/10.3934/MBE.2022110

Labani M; Afrasiabi A; Beheshti A; Lovell NH; Alinejad-Rokny H, 2022, 'PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study', Computational and Structural Biotechnology Journal, 20, pp. 4975 - 4983, http://dx.doi.org/10.1016/j.csbj.2022.09.001

Grapotte M; Saraswat M; Bessière C; Menichelli C; Ramilowski JA; Severin J; Hayashizaki Y; Itoh M; Tagami M; Murata M; Kojima-Ishiyama M; Noma S; Noguchi S; Kasukawa T; Hasegawa A; Suzuki H; Nishiyori-Sueki H; Frith MC; Abugessaisa I; Aitken S; Aken BL; Alam I; Alam T; Alasiri R; Alhendi AMN; Alinejad-Rokny H; Alvarez MJ; Andersson R; Arakawa T; Araki M; Arbel T; Archer J; Archibald AL; Arner E; Arner P; Asai K; Ashoor H; Astrom G; Babina M; Baillie JK; Bajic VB; Bajpai A; Baker S; Baldarelli RM; Balic A; Bansal M; Batagov AO; Batzoglou S; Beckhouse AG; Beltrami AP; Beltrami CA; Bertin N; Bhattacharya S; Bickel PJ; Blake JA; Blanchette M; Bodega B; Bonetti A; Bono H; Bornholdt J; Bttcher M; Bougouffa S; Boyd M; Breda J; Brombacher F; Brown JB; Bult CJ; Burroughs AM; Burt DW; Busch A; Caglio G; Califano A; Cameron CJ; Cannistraci CV; Carbone A; Carlisle AJ; Carninci P; Carter KW; Cesselli D; Chang JC; Chen JC; Chen Y; Chierici M; Christodoulou J; Ciani Y; Clark EL; Coskun M; Dalby M; Dalla E; Daub CO; Davis CA; de Hoon MJL; de Rie D; Denisenko E; Deplancke B; Detmar M; Deviatiiarov R; Di Bernardo D; Diehl AD; Dieterich LC, 2021, 'Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network', Nature Communications, 12, http://dx.doi.org/10.1038/s41467-021-23143-7

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