International journal of molecular sciences
Dec 10, 2021
Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at...
A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular finger...
Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify t...
Computational intelligence and neuroscience
Dec 10, 2021
In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method us...
Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatoria...
CPT: pharmacometrics & systems pharmacology
Dec 9, 2021
Warfarin remains the most widely prescribed oral anticoagulant in sub-Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine-learning tec...
BMC medical informatics and decision making
Dec 9, 2021
BACKGROUND: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main chal...
To predict the class level of any classification problem, predictive models are used and mostly a single predictive model is built to predict the class level of any classification problem; current research considers multiple predictive models to pred...
Data tables for machine learning and structure-activity relationship modelling (QSAR) are often naturally organized in blocks of data, where multiple molecular representations or sets of descriptors form the blocks. Multi-block Orthogonal Component A...
OBJECTIVE: The objectives of this scoping review are to identify the reliability and validity of the available tools, their limitations and any recommendations to further improve the use of these tools.
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