AIMC Topic: Deep Learning

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Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs.

Journal of X-ray science and technology
BACKGROUND: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a varie...

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis.

Journal of X-ray science and technology
BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even mo...

Developing an Image-Based Deep Learning Framework for Automatic Scoring of the Pentagon Drawing Test.

Journal of Alzheimer's disease : JAD
BACKGROUND: The Pentagon Drawing Test (PDT) is a common assessment for visuospatial function. Evaluating the PDT by artificial intelligence can improve efficiency and reliability in the big data era. This study aimed to develop a deep learning (DL) f...

Deep Learning in Therapeutic Antibody Development.

Methods in molecular biology (Clifton, N.J.)
Deep learning applied to antibody development is in its adolescence. Low data volumes and biological platform differences make it challenging to develop supervised models that can predict antibody behavior in actual commercial development steps. But ...

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.

Methods in molecular biology (Clifton, N.J.)
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their intera...

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflo...

Deep Learning Applied to Ligand-Based De Novo Drug Design.

Methods in molecular biology (Clifton, N.J.)
In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on ...

Deep Learning in Structure-Based Drug Design.

Methods in molecular biology (Clifton, N.J.)
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely...

Deep Learning and Computational Chemistry.

Methods in molecular biology (Clifton, N.J.)
Within the context of the latest resurgence in the application of artificial intelligence approaches, deep learning has undergone a renaissance over recent years. These methods have been applied to a number of problems in computational chemistry. Com...