AIMC Topic: Machine Learning

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Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

Computational and mathematical methods in medicine
A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inh...

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

PloS one
BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.

Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.

Experimental dermatology
Malignant melanoma (MM) is one of the most dangerous skin cancers. The aim of this study was to present a potential new method for the differential diagnosis of MM from melanocytic naevi (MN). We examined 20 MM and 19 MN with a new ultra-high-frequen...

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides.

Journal of chemical information and modeling
Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applicatio...

Examining the Coding Strength of Object Identity and Nonidentity Features in Human Occipito-Temporal Cortex and Convolutional Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
A visual object is characterized by multiple visual features, including its identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pat...

Transfer learning for small molecule retention predictions.

Journal of chromatography. A
Small molecule retention time prediction is a sophisticated task because of the wide variety of separation techniques resulting in fragmented data available for training machine learning models. Predictions are typically made with traditional machine...

A machine learning-based pulmonary venous obstruction prediction model using clinical data and CT image.

International journal of computer assisted radiology and surgery
PURPOSE: In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT imag...

Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks.

Scientific reports
The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron ty...

Harnessing machine learning to guide phylogenetic-tree search algorithms.

Nature communications
Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems i...

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.

Respiratory research
BACKGROUND: Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pn...