AIMC Topic: Machine Learning

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[Machine learning and its epidemiological applications].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
As an important branch of artificial intelligence, machine learning is widely used in various fields. Machine learning has similarity to classical statistical methods, but can solve many problems which are difficult for traditional statistics, so it ...

Application of machine learning in CT images and X-rays of COVID-19 pneumonia.

Medicine
Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-r...

The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare.

British medical bulletin
INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future sc...

Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet.

Mathematical biosciences and engineering : MBE
The aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithms in assessing progression fetal femur length (FL) in ultrasound images. To compare the random forest regression model with the SegNet model...

Machine learning and applications in microbiology.

FEMS microbiology reviews
To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application calle...

ℓ-norm based safe semi-supervised learning.

Mathematical biosciences and engineering : MBE
In the past few years, Safe Semi-Supervised Learning (S3L) has received considerable attentions in machine learning field. Different researchers have proposed many S3L methods for safe exploitation of risky unlabeled samples which result in performan...

Exploration of machine algorithms based on deep learning model and feature extraction.

Mathematical biosciences and engineering : MBE
The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with ...

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Briefings in bioinformatics
Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discrimina...

usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme.

Briefings in bioinformatics
Although synonymous mutations do not alter the encoded amino acids, they may impact protein function by interfering with the regulation of RNA splicing or altering transcript splicing. New progress on next-generation sequencing technologies has put t...

Machine learning approach to gene essentiality prediction: a review.

Briefings in bioinformatics
UNLABELLED: Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to...