Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
[HTML][HTML] A review on machine learning approaches and trends in drug discovery
P Carracedo-Reboredo, J Liñares-Blanco… - Computational and …, 2021 - Elsevier
Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …
treatment of diseases. In the last years, the approach used in this search presents an …
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
[HTML][HTML] Artificial intelligence in pharmaceutical sciences
Drug discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …
impacts the pharmaceutical market. However, investments in a new drug often go …
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …
development. Computational prediction of DTIs can effectively complement experimental …
EfficientNet-B4-Ranger: A novel method for greenhouse cucumber disease recognition under natural complex environment
P Zhang, L Yang, D Li - Computers and Electronics in Agriculture, 2020 - Elsevier
The intelligent identification and classification of greenhouse plant diseases is an important
research object in smart horticulture. In this study, our main task is to find an efficient method …
research object in smart horticulture. In this study, our main task is to find an efficient method …
Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations
Q Rafique, A Rehman, MS Afghan, HM Ahmad… - Computers in Biology …, 2023 - Elsevier
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug
repositioning. To reduce the experimental cost, a large number of computational …
repositioning. To reduce the experimental cost, a large number of computational …
C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods
Background and objective: Over the last two decades, DNA microarray technology has
emerged as a powerful tool for early cancer detection and prevention. It helps to provide a …
emerged as a powerful tool for early cancer detection and prevention. It helps to provide a …