AIMC Topic: Machine Learning

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Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects.

International journal of molecular sciences
Lipid metabolism has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid researc...

Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population.

Alimentary pharmacology & therapeutics
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiologic...

Automated problem list generation and physicians perspective from a pilot study.

International journal of medical informatics
OBJECTIVE: An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and ...

Automated histological classification of whole-slide images of gastric biopsy specimens.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Automated image analysis has been developed currently in the field of surgical pathology. The aim of the present study was to evaluate the classification accuracy of the e-Pathologist image analysis software.

A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.

PloS one
Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments ...

TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network.

BMC bioinformatics
BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimen...

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Computational biology and chemistry
The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular...

Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning.

IEEE transactions on medical imaging
Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due...