AIMC Topic: Machine Learning

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Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results.

International journal of molecular sciences
(1) Background: In this work, we focus on the activity of large-conductance voltage- and Ca2+-activated potassium channels (BK) from the inner mitochondrial membrane (mitoBK). The characteristic electrophysiological features of the mitoBK channels ar...

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.

Scientific reports
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions...

Dynamics of Systemic Inflammation as a Function of Developmental Stage in Pediatric Acute Liver Failure.

Frontiers in immunology
The Pediatric Acute Liver Failure (PALF) study is a multicenter, observational cohort study of infants and children diagnosed with this complex clinical syndrome. Outcomes in PALF reflect interactions among the child's clinical condition, response to...

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.

Trends in microbiology
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity w...

Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.

European radiology
OBJECTIVE: To investigate the application of machine learning-based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer.

A neurodynamic optimization approach to supervised feature selection via fractional programming.

Neural networks : the official journal of the International Neural Network Society
Feature selection is an important issue in machine learning and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some global feature selection methods based on unsupervised redundancy ...

Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning.

Applied microbiology and biotechnology
The low rate of discovery and rapid spread of resistant pathogens have made antibiotic discovery a worldwide priority. In cell-based screening, the mechanism of action (MOA) is identified after antimicrobial activity. This increases rediscovery, impa...

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges.

Sensors (Basel, Switzerland)
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause...

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Scientific reports
Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine ...

Aqueous autotaxin and TGF-βs are promising diagnostic biomarkers for distinguishing open-angle glaucoma subtypes.

Scientific reports
The purpose of this study is to examine if aqueous autotaxin (ATX) and TGF-β levels could be used for differentiating glaucoma subtypes. This prospective observational study was performed using aqueous humor samples obtained from 281 consecutive pati...