AIMC Topic: Machine Learning

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ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Computational biology and chemistry
Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exh...

Re-investigation of functional gastrointestinal disorders utilizing a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high ov...

Artificial intelligence in foot and ankle surgery: current concepts.

Orthopadie (Heidelberg, Germany)
The twenty-first century has proven that data are the new gold. Artificial intelligence (AI) driven technologies might potentially change the clinical practice in all medical specialities, including orthopedic surgery. AI has a broad spectrum of subc...

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM concentrations over India.

Chemosphere
The spatial coverage of PM monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalys...

Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.

Journal of medicinal chemistry
Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusio...

Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training.

Journal of chemical information and modeling
A key aspect of producing accurate and reliable machine learning models for the prediction of properties of quantum chemistry (QC) data is identifying possible data characteristics that may negatively influence model training. In previous work, we id...

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning.

PLoS computational biology
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through...

Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.

Hereditas
BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in ...

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

PloS one
Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As...

Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

Environmental science and pollution research international
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a ...