AIMC Topic: Machine Learning

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M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines.

International journal of pharmaceutics
Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) three-dimensional printing (3DP) has made signifi...

Selecting machine-learning scoring functions for structure-based virtual screening.

Drug discovery today. Technologies
Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover...

Using Chou's 5-steps rule to identify N-methyladenine sites by ensemble learning combined with multiple feature extraction methods.

Journal of biomolecular structure & dynamics
-methyladenine (m6A), a type of modification mostly affecting the downstream biological functions and determining the levels of gene expression, is mediated by the methylation of adenine in nucleic acids. It is also a key factor for influencing biolo...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

Surgery
BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these...

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.

Biochemical and biophysical research communications
Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict...

Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.

Neuroimaging clinics of North America
Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the ma...

Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection.

Computers in biology and medicine
Class imbalance and the presence of irrelevant or redundant features in training data can pose serious challenges to the development of a classification framework. This paper proposes a framework for developing a Clinical Decision Support System (CDS...

Learning probabilistic neural representations with randomly connected circuits.

Proceedings of the National Academy of Sciences of the United States of America
The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is th...

Amplification Curve Analysis: Data-Driven Multiplexing Using Real-Time Digital PCR.

Analytical chemistry
Information about the kinetics of PCR reactions is encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we d...

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning.

Sensors (Basel, Switzerland)
Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting hea...