AIMC Topic: Deep Learning

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EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.

Journal of chemical information and modeling
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enha...

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery.

Journal of chemical information and modeling
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactio...

An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods.

Journal of environmental management
Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water ...

Contextual AI models for single-cell protein biology.

Nature methods
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challen...

Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction.

The journal of physical chemistry letters
Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significantly advanced the field; however, these methods exhibit limitations in predictive performance and the ...

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.

BMC cancer
BACKGROUND: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tum...

DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.

BMC medical informatics and decision making
Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks...

Radiograph-based rheumatoid arthritis diagnosis via convolutional neural network.

BMC medical imaging
OBJECTIVES: Rheumatoid arthritis (RA) is a severe and common autoimmune disease. Conventional diagnostic methods are often subjective, error-prone, and repetitive works. There is an urgent need for a method to detect RA accurately. Therefore, this st...

YOLO-Granada: a lightweight attentioned Yolo for pomegranates fruit detection.

Scientific reports
Pomegranate is an important fruit crop that is usually managed manually through experience. Intelligent management systems for pomegranate orchards can improve yields and address labor shortages. Fast and accurate detection of pomegranates is one of ...

Genome composition-based deep learning predicts oncogenic potential of HPVs.

Frontiers in cellular and infection microbiology
Human papillomaviruses (HPVs) account for more than 30% of cancer cases, with definite identification of the oncogenic role of viral and genes. However, the identification of high-risk HPV genotypes has largely relied on lagged biological explorati...