AIMC Topic: Machine Learning

Clear Filters Showing 23851 to 23860 of 34417 articles

Machine learning-assisted directed protein evolution with combinatorial libraries.

Proceedings of the National Academy of Sciences of the United States of America
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequ...

Information Transmitted From Bioinspired Neuron-Astrocyte Network Improves Cortical Spiking Network's Pattern Recognition Performance.

IEEE transactions on neural networks and learning systems
We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron-astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 7...

Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

Computer methods and programs in biomedicine
BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and t...

Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression.

Journal of translational medicine
BACKGROUND: Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is incon...

Expression of socially sensitive genes: The multi-ethnic study of atherosclerosis.

PloS one
BACKGROUND: Gene expression may be an important biological mediator in associations between social factors and health. However, previous studies were limited by small sample sizes and use of differing cell types with heterogeneous expression patterns...

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope Diagnostic System (MMDx). The present study was designed to optimize the accuracy and stabilit...

Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

Computer methods and programs in biomedicine
OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative ...

Fast and reliable determination of Escherichia coli susceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms.

Journal of biophotonics
Antimicrobial drugs have an important role in controlling bacterial infectious diseases. However, the increasing resistance of bacteria to antibiotics has become a global health care problem. Rapid determination of antimicrobial susceptibility of cli...

Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.

Diabetes technology & therapeutics
The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U...