AIMC Topic: Machine Learning

Clear Filters Showing 17531 to 17540 of 34417 articles

Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care.

International journal of medical informatics
BACKGROUND: Patient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not ca...

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.

Nature communications
Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning mo...

MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data.

PLoS computational biology
The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplas...

A Machine Learning Approach to Predictive Modelling of Student Performance.

F1000Research
- Many factors affect student performance such as the individual's background, habits, absenteeism and social activities. Using these factors, corrective actions can be determined to improve their performance. This study looks into the effects of th...

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms.

F1000Research
: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential...

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer.

Computational intelligence and neuroscience
Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for p...

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods.

Computational intelligence and neuroscience
BACKGROUND: Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of ...

Call for a Public Open Database of All Chemical Reactions.

Journal of chemical information and modeling
Today there exists no public, freely downloadable, comprehensive database of all known chemical reactions and associated information. Such a database not only would serve chemical sciences and technologies around the world but also would enable the p...

Discriminating Neoplastic from Nonneoplastic Tissues Using an miRNA-Based Deep Cancer Classifier.

The American journal of pathology
Next-generation sequencing has enabled the collection of large biological data sets, allowing novel molecular-based classification methods to be developed for increased understanding of disease. miRNAs are small regulatory RNA molecules that can be q...

Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (...