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Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases

Lan, A. Y., & Corces, M. R. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1027224 · Cited by 16 · see citing works

Deep multi-scale Gaussian residual networks for contextual-aware translation initiation site recognition

Guo, Y., Zhou, D., Li, W., & Cao, J.-D. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.118004 · Cited by 16 · see citing works

A review of AI-based radiogenomics in neurodegenerative disease

Liu, H., Zhang, X., & Liu, Q. (2025) · Frontiers in Big Data · doi:10.3389/fdata.2025.1515341 · Cited by 14 · see citing works

A New Deep Learning Model based on Neuroimaging for Predicting Alzheimer's Disease

Kiran, P., Sudheesh, K. V., Ravi, V., Almeshari, M. Y., Alzamil, Y., S, S. K. D., & Harshitha, R. (2023) · The Open Bioinformatics Journal · doi:10.2174/0118750362260635230922051326 · Cited by 14 · see citing works

Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability

Zhou, X., Kedia, S., Meng, R., & Gerstein, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312848 · Cited by 14 · see citing works

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction

Luo, H., Liang, H., Liu, H., Fan, Z., Wei, Y., Yao, X., & Cong, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031655 · Cited by 14 · see citing works

Using Bayesian Optimization to Effectively Tune Random Forest and XGBoost Hyperparameters for Early Alzheimer’s Disease Diagnosis

Bloch, L., & Friedrich, C. M. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-70569-5_18 · Cited by 13 · see citing works

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Ho, N.-H., Jeong, Y.-H., & Kim, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-37500-7 · Cited by 13 · see citing works

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model

Ngai, T., Willett, J., Waqas, M., Fishbein, L., Choi, Y., Hahn, G., Mullin, K., Lange, C., Hecker, J., Tanzi, R. E., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14319 · Cited by 12 · see citing works

Alzheimer’s disease prediction using three machine learning methods

Ahmed, S. T., & Kadhem, S. M. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v27.i3.pp1689-1697 · Cited by 12 · see citing works

Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research

Gu, Z., Ge, B., Wang, Y., Gong, Y., & Qi, M. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1609063 · Cited by 12 · see citing works

Automated classification of Alzheimer's disease based on deep belief neural networks

Nanthini, K., Tamilarasi, A., Sivabalaselvamani, D., & Suresh, P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09468-6 · Cited by 11 · see citing works

Hybrid cuttle Fish-Grey wolf optimization tuned weighted ensemble classifier for Alzheimer’s disease classification

Mandawkar, U., & Diwan, T. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106101 · Cited by 11 · see citing works

Alzheimer’s Disease Classification Using Genetic Data

Khanal, S., Chen, J., Jacobs, N., & Lin, A. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669730 · Cited by 10 · see citing works

Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions

Krokidis, M. G., Vrahatis, A. G., Lazaros, K., Skolariki, K., Exarchos, T. P., & Vlamos, P. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45110544 · Cited by 10 · see citing works

Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation

Sarma, M., & Chatterjee, S. (2025) · Diagnostics · doi:10.3390/diagnostics15020211 · Cited by 10 · see citing works

A review of multimodal medical data fusion techniques for personalized medicine

Liu, C., & Ye, F. (2025) · International Conference on Biomedical and Intelligent Systems (IC-BIS) · doi:10.1145/3745034.3745088 · Cited by 10 · see citing works

Microarray Gene Expression Data for Detection Alzheimer’s Disease Using k-means and Deep Learning

AL-Bermany, H. M., & AL-Rashid, S. Z. (2021) · International Engineering Conference on Sustainable Technology and Development (IEC) · doi:10.1109/iec52205.2021.9476128 · Cited by 10 · see citing works

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

El-Gawady, A., Tawfik, B. B. S., & Makhlouf, M. A. A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.034734 · Cited by 9 · see citing works

Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology

Gunashekar, D. D., Bielak, L., Oerther, B., Benndorf, M., Nedelcu, A., Hickey, S., Zamboglou, C., Grosu, A., & Bock, M. (2024) · Radiation Oncology · doi:10.1186/s13014-024-02471-0 · Cited by 9 · see citing works

UnCOT-AD: Unpaired Cross-Omics Translation Enables Multi-Omics Integration for Alzheimer’s Disease Prediction

Abir, A. R., Dip, S. A., & Zhang, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf438 · Cited by 9 · see citing works

A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer’s Disease

Akkaya, U. M., & Kalkan, H. (2023) · Biomolecules · doi:10.3390/biom13111563 · Cited by 11 · see citing works

Neural Network Aided Detection of Huntington Disease

Alfonso, G., & Caballero‐Villarraso, J. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082110 · Cited by 9 · see citing works

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

Ahmed, S. T., & Kadhem, S. M. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i2.pp2052-2067 · Cited by 9 · see citing works

Early Diagnosis Model of Alzheimer’s Disease Based on Hybrid Meta-Heuristic with Regression Based Multi Feed Forward Neural Network

Rajasekhar, B. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10346-y · Cited by 8 · see citing works

Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques

Alfonso, G., & Caballero‐Villarraso, J. (2021) · Mathematics · doi:10.3390/math9192482 · Cited by 8 · see citing works

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati, G. M. F., & Algaphari, G. H. (2021) · International Journal of Computer Systems & Software Engineering · doi:10.15282/ijsecs.7.2.2021.4.0087 · Cited by 8 · see citing works

Potential Molecular Mechanisms of Alzheimer’s Disease from Genetic Studies

Nwadiugwu, M. C., Shen, H., & Deng, H. (2023) · Biology · doi:10.3390/biology12040602 · Cited by 8 · see citing works

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data

Qiu, W., Qi, B.-B., Lin, W., Zhang, S., Yu, W., & Huang, S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.926927 · Cited by 7 · see citing works

End-to-End Clustering Enhanced Contrastive Learning for Radiology Reports Generation

Liu, X., Xin, J., Shen, Q., Li, C., Huang, Z., & Wang, Z. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3449876 · Cited by 7 · see citing works

Etiology of Late-Onset Alzheimer’s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis

Sarma, M., & Chatterjee, S. (2024) · Diagnostics · doi:10.3390/diagnostics14232640 · Cited by 7 · see citing works

Integration of gene expression and DNA methylation data using MLA-GNN for liver cancer biomarker mining

Lu, C.-Y., Liu, Z., Arif, M., Alam, T., & Qiu, W. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1513938 · Cited by 7 · see citing works

A Hybrid mRMR-RFE and AI Framework for Advancing Alzheimer's Biomarkers Discovery

Maniruzzaman, M., Jaman, M. S., Abid, M. A. S., Mahmud, Z., Rahman, M. E., & Siddiky, M. N. A. (2025) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic64266.2025.10920700 · Cited by 7 · see citing works

A new dimensionality reduction technique based on the Wavelet Transform for cancer classification

Fernández, L., Pérez, M., Orduña, J. M., & Alcaraz, J. M. (2025) · Journal Of Big Data · doi:10.1186/s40537-024-01039-9 · Cited by 6 · see citing works

Limitations and opportunities in multi-omics integration for neurodevelopmental, neurodegenerative and psychiatric disorders: A systematic review

Behrens, L. M. P., Fernandes, G. D. S., Gonçalves, G. F., Nunes, F. V. M., Weimer, R. D., Moreira, J. C. F., & Dorn, M. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.01.019 · Cited by 8 · see citing works

TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic

Du, L., Gao, P., Liu, Z., Yin, N., & Wang, X. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108202 · Cited by 6 · see citing works

Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems

Babichev, S. A., Liakh, I., & Škvor, J. (2025) · IEEE Access · doi:10.1109/access.2025.3535999 · Cited by 6 · see citing works

AD-GCN: A novel graph convolutional network integrating multi-omics data for enhanced Alzheimer’s disease diagnosis

Li, Z., Li, Q., Li, X., Luo, W. F., Guo, H., Zhao, C., Yang, C., Xie, A., Hu, K., & Guo, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325050 · Cited by 6 · see citing works

Alzheimer's Disease Classification Using Deep Learning

Ftoutou, A., Majdoub, N., & Ladhari, T. (2023) · IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE) · doi:10.1109/icaige58321.2023.10346502 · Cited by 6 · see citing works

Leveraging SVD Entropy and Explainable Machine Learning for Alzheimer's and Frontotemporal Dementia Detection using EEGPreprint

Lal, U., Chikkankod, A. V., & Longo, L. (2023) · doi:10.36227/techrxiv.23992554.v2 · Cited by 6 · see citing works

Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data

Mahendran, N., & Vincent P M, D. R. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.021 · Cited by 31 · see citing works

Alzheimer's disease prediction based on continuous feature representation using multi-omics data integration

Abbas, Z., Tayara, H., & Chong, K. T. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104536 · Cited by 31 · see citing works

MuloAD: A Multiomics Integration Model Utilizing Graph Convolutional Networks for Alzheimer's Disease Diagnosis and Biomarker Identification

Zhou, H., Zhu, Y., Shi, X., & Wang, L. (2026) · European Journal of Neuroscience · doi:10.1111/ejn.70452 · Cited by 2 · see citing works

Deep Neural Network Classifier for Alzheimer’s Disease

Lin, J., Lee, H., & Snyder, M. P. (2022) · Journal of Student Research · doi:10.47611/jsrhs.v11i3.3553

Novel approach to early prediction of alzheimer’s disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks

Roobini, S., Kavitha, M. S., & Karthik, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-55178-5 · Cited by 1 · see citing works