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Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Sakshi Gautam, Anamika Thakur, Akanksha Rajput, Manoj Kumar

Viruses 2023 cited by 30

Medicine & Health Computational Drug Discovery Methods ai assisted importance 7/10 scale stated

Dengue virus has no approved antivirals, so computational methods are needed to predict small-molecule inhibitors and repurposed drug candidates.

Details Source Open-access PDF model: SVM, ANN, kNN, RF (best model not specifie...

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy

Thiraphat Tanphiriyakun, Sattaya Rojanasthien, Piyapong Khumrin

Scientific Reports 2021 cited by 39

Medicine & Health Bone health and osteoporosis research ai assisted importance 7/10 scale stated

Doctors lack a reliable way to select the best osteoporosis treatment regimen for individual patients to improve bone mineral density and prevent fractures.

Details Source Open-access PDF model: Random Forests

Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

Dimitrios Vitsios, Ryan S. Dhindsa, Lawrence Middleton, Ayal B. Gussow, Slavé Petrovski

Nature Communications 2021 cited by 88

Biology & Life Sciences Genomics and Rare Diseases partially solved importance 7/10 scale stated

Identifying which non-coding regions of the human genome are functional and disease-relevant is a key challenge in genomics.

Details Source Open-access PDF model: JARVIS (deep learning model)

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram

Akhil Vaid, Kipp W. Johnson, Marcus A. Badgeley, Sulaiman S. Somani, Mesude Bicak, Isotta Landi, Adam J. Russak, Shan Zhao et al.

JACC. Cardiovascular imaging 2021 cited by 124

Medicine & Health ECG Monitoring and Analysis ai assisted importance 7/10 scale stated

Rapidly assessing left and right ventricular (heart pump) function from a routine ECG was not possible; no deep learning tools existed for right ventricular function and left ventricular tools covered only very low function.

Details Source Open-access PDF model: not stated in source

Random forest-based prediction of stroke outcome

Carlos Fernandez-Lozano, Pablo Hervella, Virginia Mato‐Abad, Manuel Rodríguez‐Yáñez, Sonia Suárez, Iria López‐Dequidt, Ana Estany‐Gestal, Tomás Sobrino et al.

Scientific Reports 2021 cited by 130

Medicine & Health Acute Ischemic Stroke Management ai assisted importance 7/10 scale stated

Clinicians lack tools to predict which stroke patients will die or suffer morbidity three months after admission.

Details Source Open-access PDF model: Random Forest

A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans

Xiyue Wang, Tao Shen, Sen Yang, Jun Lan, Yanming Xu, Minghui Wang, Jing Zhang, Xiao Han

NeuroImage Clinical 2021 cited by 192

Medicine & Health Intracerebral and Subarachnoid Hemorrhage Research partially solved importance 7/10 scale stated

Diagnosing acute intracranial hemorrhage on non-contrast head CT varies greatly among radiologists due to subtle findings and heavy time pressure.

Details Source Open-access PDF model: 2D CNN plus two sequence models (names not...

MVP predicts the pathogenicity of missense variants by deep learning

Hongjian Qi, Haicang Zhang, Yige Zhao, Chen Chen, John J. Long, Wendy K. Chung, Yongtao Guan, Yufeng Shen

Nature Communications 2021 cited by 198

Medicine & Health Genomics and Rare Diseases partially solved importance 7/10 scale stated

Accurately predicting whether missense genetic variants are pathogenic is critical for genetic studies and clinical diagnosis, and prior methods had limited performance.

Details Source Open-access PDF model: MVP (deep residual network)

Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

Steve Rodriguez, Clemens B. Hug, Petar V. Todorov, Nienke Moret, Sarah A. Boswell, Kyle E. Evans, George Zhou, Nathan T. Johnson et al.

Nature Communications 2021 cited by 293

Medicine & Health Computational Drug Discovery Methods ai assisted importance 7/10 scale stated

Alzheimer's disease drug trials are slow, expensive and largely failing, so existing approved drugs need to be screened for reuse.

Details Source Open-access PDF model: DRIAD

Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

Jonathan A Weyn, Dale Richard Durran, Rich Caruana, Nathaniel Cresswell‐Clay

Journal of Advances in Modeling Earth Systems 2021 cited by 190

Climate & Environment Meteorological Phenomena and Simulations partially solved importance 7/10 scale stated

Producing sub-seasonal (six-week) global weather forecasts with large ensembles is computationally prohibitive with traditional numerical models.

Details Source Open-access PDF model: DLWP (convolutional neural networks on cub...

Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system

Xin Zhang, Kai Gu, Shumei Miao, Xiaoliang Zhang, Yuechuchu Yin, Cheng Wan, Yun Yu, Jie Hu et al.

Cardiovascular Diagnosis and Therapy 2020 cited by 81

Medicine & Health ECG Monitoring and Analysis ai assisted importance 7/10 scale stated

Manual ECG interpretation is error-prone; automated detection and classification of cardiovascular disease from ECG signals was needed.

Details Source Open-access PDF model: Convolutional neural network (CNN)

Optimizing Quantum Error Correction Codes with Reinforcement Learning

Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, Nicolai Friis

Quantum 2019 cited by 158

Physics & Astronomy Quantum Computing Algorithms and Architecture partially solved importance 7/10 scale stated

Determining the most suited quantum error correction encoding for unknown error channels or specific laboratory setups is highly challenging.

Details Source Open-access PDF model: reinforcement learning agent (specific alg...

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

Pushpanjali Gupta, Sum-Fu Chiang, Prasan Kumar Sahoo, Suvendu Kumar Mohapatra, Jeng‐Fu You, Djeane Debora Onthoni, Hsin‐Yuan Hung, Jy‐Ming Chiang et al.

Cancers 2019 cited by 100

Medicine & Health AI in cancer detection partially solved importance 7/10 scale stated

Predicting colon cancer TNM stage and five-year disease-free survival from histopathology parameters is difficult.

Details Source Open-access PDF model: Random Forest (among various ML algorithms...

Deep learning quantifies pathologists’ visual patterns for whole slide image diagnosis

Tianhang Nan, Song Zheng, Siyuan Qiao, Hao Quan, Xin Gao, Xin Gao, Jun Niu, Bin Zheng et al.

Nature Communications 2025 cited by 45

Medicine & Health AI in cancer detection solved importance 7/10 scale stated

Training deep learning diagnostic models on whole slide images requires massive pixelwise annotation time from pathologists, limiting dataset size and diagnostic precision.

Details Source Open-access PDF model: PEAN (Pathology Expertise Acquisition Netw...

A scoping review of artificial intelligence applications in clinical trial risk assessment

Douglas Teodoro, Nona Naderi, Anthony Yazdani, Boya Zhang, Alban Bornet

npj Digital Medicine 2025 cited by 46

Medicine & Health Artificial Intelligence in Healthcare and Education partially solved importance 7/10 scale stated

Assessing safety, efficacy, and operational risks in clinical trials is difficult; AI is increasingly applied to improve it.

Details Source Open-access PDF model: Multiple reviewed: machine learning, graph...

Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

Ziyao Meng, Zhouyu Guan, Shujie Yu, Yi-Lan Wu, Yaoning Zhao, Jie Shen, Cynthia Ciwei Lim, Ting‐Li Chen et al.

The Lancet Digital Health 2025 cited by 47

Medicine & Health Retinal Imaging and Analysis partially solved importance 7/10 scale stated

Screening for diabetic kidney disease and distinguishing isolated diabetic nephropathy from non-diabetic kidney disease are major access-limited challenges in diabetes care.

Details Source Open-access PDF model: DeepDKD (deep learning system)

Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

Ya Zhang, Zheng Li, Zhongqiang Li, Huaizhi Wang, Dinkar Regmi, Jian Zhang, Ji‐Ming Feng, Shaomian Yao et al.

Biological Procedures Online 2024 cited by 15

Medicine & Health Spectroscopy Techniques in Biomedical and Chemical Research ai assisted importance 7/10 scale stated

Surgeons need to quickly distinguish cancerous breast tissue from healthy tissue during surgery to ensure complete tumor removal.

Details Source Open-access PDF model: Random Forest, Support Vector Machine, Con...

De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model

Haohuai He, Bing He, Lei Guan, Yu Zhao, Feng Jiang, Guanxing Chen, Qing-Ge Zhu, Calvin Yu‐Chian Chen et al.

Nature Communications 2024 cited by 69

Medicine & Health vaccines and immunoinformatics approaches partially solved importance 7/10 scale stated

Discovering antigen-specific antibodies relies on isolating them from serum, a resource-intensive and time-consuming process.

Details Source Open-access PDF model: PALM-H3; A2binder

Prospective de novo drug design with deep interactome learning

Kenneth Atz, Leandro Cotos, Clemens Isert, M. Håkansson, Dorota Focht, Mattis Hilleke, David F. Nippa, Michael Iff et al.

Nature Communications 2024 cited by 95

Medicine & Health Computational Drug Discovery Methods partially solved importance 7/10 scale stated

Designing new drug-like molecules from scratch with specific bioactivity, synthesizability and structural novelty is a core challenge in drug discovery.

Details Source Open-access PDF model: Graph neural networks plus chemical langua...

Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

Guillaume P. Ramstein, Edward S. Buckler

Genome biology 2022 cited by 34

Biology & Life Sciences Genetic Mapping and Diversity in Plants and Animals ai assisted importance 7/10 scale stated

Identifying which maize genetic variants are functionally important, at high resolution, to support crop improvement via genomic prediction and genome editing.

Details Source Open-access PDF model: deep learning (specific architecture not s...

Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

David Domínguez, Luis de Juan del Villar, Odette Pantoja Díaz, Mario González

Sustainability 2022 cited by 32

Climate & Environment Conservation, Biodiversity, and Resource Management partially solved importance 7/10 scale stated

Predicting annual Amazon rain-forest deforestation area across 760 Brazilian municipalities from static geographic and 20-year time-series data.

Details Source Open-access PDF model: Hybrid dense neural network plus LSTM