By Dr Hamid Alinejad-Rokny

Preprints

Chen S; Zahedi R; Chhuo L; Nguyen R; BaghGolshani M; Beheshti A; Grosser M; Yang M; Farbehi N; Lovell N; Argha A; Porntaveetusm T; Vafaee F; Ye Y; Alinejad-Rokny H, 2026, Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation, http://dx.doi.org/10.48550/arxiv.2607.17227

Liang Y; Chhuo L; Argha A; Farbehi N; Chen L; Alizadehsani R; Hosseinzadeh M; Yang M; Porntaveetusm T; Ye Y; Alinejad-Rokny H, 2026, Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability, http://dx.doi.org/10.48550/arxiv.2604.05478

Shamsi A; Guo X-Y; Alinejad-Rokny H; Mohammadi A; Teney D; Abbasnejad E, 2026, Multi-Hypothesis Test-Time Adaptation to Mitigate Underspecification, http://dx.doi.org/10.48550/arxiv.2607.00259

Li S; Fan L; Li Z; Wan Z; Lin Y; Ni S; Fang F; Alinejad-Rokny H; Song Y; Jing K; Zhang CJ; Yang M, 2026, SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models, http://dx.doi.org/10.48550/arxiv.2606.29815

Xu A; Yang Z; Li J; Yuan G; Chen L; Yan L; Zhou J; Qin Z; Chang H; Chen Y; Alinejad-Rokny H; Yang M, 2026, EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection, http://dx.doi.org/10.48550/arxiv.2505.17654

Li S; Shi J; Ni S; Zhang G; Li S; Wang S; Wen Z; Li Y; Alinejad-Rokny H; Liu J; Yang M; Huang W, 2026, CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs, http://dx.doi.org/10.48550/arxiv.2603.07078

Luo R; Lin T-E; Zhang H; Wu Y; Liu X; Yang M; Li Y; Chen L; Li J; Zhang L; Xia X; Alinejad-Rokny H; Huang F, 2025, OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis, http://dx.doi.org/10.48550/arxiv.2501.04561

Zahedi R; Argha A; Farbehi N; Bakhshayeshi I; Ye Y; Lovell NH; Alinejad-Rokny H, 2025, SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics, http://dx.doi.org/10.48550/arxiv.2506.11491

Doan BG; Shamsi A; Guo X-Y; Mohammadi A; Alinejad-Rokny H; Sejdinovic D; Teney D; Ranasinghe DC; Abbasnejad E, 2025, Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks, http://dx.doi.org/10.48550/arxiv.2407.20891

Lee S; Zhou J; Ao C; Li K; Du X; He S; Wu H; Liu T; Liu J; Alinejad-Rokny H; Yang M; Liang Y; Wen Z; Ni S, 2025, Quantification of Large Language Model Distillation, http://dx.doi.org/10.48550/arxiv.2501.12619

Lee S; Ni S; Wei C; Li S; Fan L; Argha A; Alinejad-Rokny H; Xu R; Gong Y; Yang M, 2025, xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking, http://dx.doi.org/10.48550/arxiv.2501.16727

Wang Q; Ni S; Liu H; Lu S; Chen G; Feng X; Wei C; Qu Q; Alinejad-Rokny H; Lin Y; Yang M, 2024, AutoPatent: A Multi-Agent Framework for Automatic Patent Generation, http://dx.doi.org/10.48550/arxiv.2412.09796

Ni S; Wu H; Yang D; Qu Q; Alinejad-Rokny H; Yang M, 2024, Small Language Model as Data Prospector for Large Language Model, http://dx.doi.org/10.48550/arxiv.2412.09990

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

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

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

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

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

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, 2023, 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

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

Nasab RZ; Ghamsari MRE; Argha A; Macphillamy C; Beheshti A; Alizadehsani R; Lovell NH; Lotfollahi M; Alinejad-Rokny H, 2023, Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View, http://dx.doi.org/10.48550/arxiv.2210.04453

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

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

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

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

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

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

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

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

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

Alinejad-Rokny H; Heng JIT; Forrest ARR, 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 ARR; Alinejad-Rokny H, 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

Subramanian S; Thoms JAI; Huang Y; Cornejo P; Koch FC; Jacquelin S; Shen S; Song E; Joshi S; Brownlee C; Woll PS; Fajardo DC; Beck D; Curtis DJ; Yehson K; Antonenas V; Brien TO; Trickett A; Powell JA; Lewis ID; Pitson SM; Gandhi MK; Lane SW; Vafaee F; Wong ES; Göttgens B; Rokny HA; Wong JWH; Pimanda JE, 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

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

Ghamsari R; de Graaf CA; Thijssen R; You Y; Lovell NH; Alinejad-Rokny H; Ritchie ME, Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs, http://dx.doi.org/10.1101/2025.09.08.675012

Khan SI; Rahman A; Karim R; Bithi NI; Band SS; Dehzangi A; Alinejad-Rokny H, 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

Hansun S; Argha A; Bakhshayeshi I; Wicaksana A; Alinejad-Rokny H; Fox GJ; Liaw S-T; Celler BG; Marks GB, Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review (Preprint), http://dx.doi.org/10.2196/preprints.69068

Asgari Y; Heng JIT; Lovell N; Forrest ARR; Alinejad-Rokny H, 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

Truong P; Shen S; Joshi S; Islam I; 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, 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

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

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

Afrasiabi A; Alinejad-Rokny H; Lovell N; Xu Z; Ebrahimi D, Insight into the origin of 5’UTR and source of CpG reduction in SARS-CoV-2 genome, http://dx.doi.org/10.1101/2020.10.23.351353

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

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

Alinejad-Rokny H; Modegh RG; Rabiee HR; Rezaie N; Tam KT; Forrest ARR, 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

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

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

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