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AI-Driven Deep Learning Techniques in Protein Structure Prediction

Lingtao Chen, Qiaomu Li, Kazi Fahim Ahmad Nasif, Ying Xie, Bobin Deng, Shuteng Niu, Seyedamin Pouriyeh, Zhiyu Dai et al.

International Journal of Molecular Sciences 2024 cited by 136

Biology & Life Sciences Protein Structure and Dynamics partially solved importance 8/10 scale stated

Predicting protein 3D structure from sequence is a long-standing computational challenge important for understanding function.

Details Source Open-access PDF model: AlphaFold (1-3), RoseTTAFold, ProteinBERT

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

Israa M. Hayder, Taief Alaa Al-Amiedy, Wad Ghaban, Faisal Saeed, Maged Nasser, Ghazwan Abdulnabi Al-Ali, Hussain A. Younis

Processes 2023 cited by 74

Climate & Environment Hydrological Forecasting Using AI ai assisted importance 8/10 scale stated

Floods cause casualties worldwide, and accurate precipitation and flood forecasting is needed to reduce damage and save lives.

Details Source Open-access PDF model: ES-LSTM, RNN, ANN, decision tree

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Natei Ermias Benti, Mesfin Diro Chaka, Addisu Gezahegn Semie

Sustainability 2023 cited by 378

Climate & Environment Energy Load and Power Forecasting ai assisted importance 8/10 scale stated

Renewable energy generation is variable and uncertain, making accurate forecasting difficult, which complicates efficient grid operation and energy management.

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

FuXi: a cascade machine learning forecasting system for 15-day global weather forecast

Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Qi Yuan, Hao Li

npj Climate and Atmospheric Science 2023 cited by 355

Climate & Environment Meteorological Phenomena and Simulations partially solved importance 8/10 scale stated

Medium-range weather forecasts accumulate errors beyond 10 days, so ML models still lag the ECMWF ensemble for 15-day forecasts.

Details Source Open-access PDF model: FuXi (cascaded machine learning system)

A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression

Yi Cong, Misaki Shintani, Fuga Imanari, Naoki Osada, Toshinori Endo

OMICS A Journal of Integrative Biology 2022 cited by 40

Medicine & Health Computational Drug Discovery Methods ai assisted importance 8/10 scale stated

Finding new uses for existing drugs is slow and costly; the study aimed to identify repurposing candidates by linking diseases through shared gene-expression patterns.

Details Source Open-access PDF model: Two-stage prediction with UMAP and k-means...

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Carles Rubio Maturana, Allisson Dantas de Oliveira, Sergi Nadal, Besim Bilalli, Francesc Zarzuela, Mateu Espasa, Elena Sulleiro, Mercedes Bosch et al.

Frontiers in Microbiology 2022 cited by 87

Medicine & Health Digital Imaging for Blood Diseases ai assisted importance 8/10 scale stated

Malaria diagnosis by manual microscopy is slow and requires a well-trained microscopist, limiting diagnosis in resource-poor settings.

Details Source Open-access PDF model: Convolutional Neural Networks

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Youngjun Park, Dominik Heider, Anne-Christin Hauschild

Cancers 2021 cited by 41

Medicine & Health Bioinformatics and Genomic Networks ai assisted importance 8/10 scale stated

Large-scale next-generation sequencing data in cancer research creates big-data challenges, requiring sophisticated analysis to characterize tumors and tumor subtypes.

Details Source Open-access PDF model: deep neural networks; graph neural network...

Identification of Novel Antibacterials Using Machine Learning Techniques

Yan A. Ivanenkov, Alex A. Zhavoronkov, Renat S. Yamidanov, Ilya А. Osterman, Сергиев Петр Владимирович, Vladimir Aladinskiy, Anastasia V. Aladinskaya, Victor A. Terentiev et al.

Frontiers in Pharmacology 2019 cited by 47

Medicine & Health Computational Drug Discovery Methods ai assisted importance 8/10 scale stated

Pharmaceutical companies avoid developing novel antibacterials despite urgent need for new antibiotics against resistant bacterial strains.

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

Machine Learning in Carbon Capture, Utilization, Storage, and Transportation: A Review of Applications in Greenhouse Gas Emissions Reduction

Xuejia Du, Muhammad Noman Khan, Ganesh C. Thakur

Processes 2025 cited by 25

Climate & Environment Carbon Dioxide Capture Technologies ai assisted importance 8/10 scale stated

CCUS processes face technical and economic challenges in optimization and scalability that hinder reducing greenhouse gas emissions.

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

Matrix Factorization-based Technique for Drug Repurposing Predictions

Gaia Ceddia, Pietro Pinoli, Stefano Ceri, Marco Masseroli

IEEE Journal of Biomedical and Health Informatics 2020 cited by 56

Medicine & Health Computational Drug Discovery Methods ai assisted importance 8/10 scale stated

Developing new drugs is hugely costly and slow, with about 85% of proposed molecules failing the first three FDA approval phases, so finding new indications for already approved drugs is needed.

Details Source Open-access PDF model: Non-negative Matrix Tri-Factorization (NMT...

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Buket Baddal, Ferdiye Taner, Dilber Uzun Ozsahin

Diagnostics 2024 cited by 46

Medicine & Health Artificial Intelligence in Healthcare and Education ai assisted importance 8/10 scale stated

Healthcare-associated infections are the most common adverse events in healthcare, and conventional surveillance is costly and labor intensive.

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

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, Bingding Huang

Diagnostics 2022 cited by 129

Medicine & Health AI in cancer detection ai assisted importance 8/10 scale stated

Lung cancer has one of the highest mortality rates of all cancers, and early detection of pulmonary nodules in CT scans is crucial for survival. The paper reviews computer-aided diagnosis systems that detect and classify such nodules.

Details Source Open-access PDF model: CNN architectures (reviewed, e.g., multivi...