AIMC Topic: Machine Learning

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A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...

Machine learning approaches to debris flow susceptibility analyses in the Yunnan section of the Nujiang River Basin.

PeerJ
BACKGROUND: The Yunnan section of the Nujiang River (YNR) Basin in the alpine-valley area is one of the most critical areas of debris flow in China.

Machine learning-based classification of petrofacies in fine laminated limestones.

Anais da Academia Brasileira de Ciencias
Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a tes...

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

PloS one
In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

PloS one
Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medi...

A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

PloS one
Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it i...

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Neural decoding is still a challenging and a hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatiotemporal structural information represent the brain's activation information under e...

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers...

Unlocking the full potential of mesenchymal stromal cell therapy for osteoarthritis through machine learning-based in silico trials.

Cytotherapy
Despite the potential of mesenchymal stromal cells (MSCs) in osteoarthritis (OA) treatment, the challenge lies in addressing their therapeutic inconsistency. Clinical trials revealed significantly varied therapeutic outcomes among patients receiving ...

Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods.

Biosensors & bioelectronics
We present an electrochemical platform designed to reduce time of Escherichia coli bacteria detection from 24 to 48-h to 30 min. The presented approach is based on a system which includes gallium-indium (eGaIn) alloy to provide conductivity and a hyd...