AIMC Topic: Machine Learning

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Omics in IgG4-related disease.

Chinese medical journal
Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and furth...

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.

Computers in biology and medicine
Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer "hormone receptor-positive and HER2-negative, hormone receptor-positive...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Obesity surgery
PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver...

Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension.

Journal of biomedical informatics
OBJECTIVE: Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a stra...

A Machine-Learning Approach to Biosignature Exploration on Early Earth and Mars Using Sulfur Isotope and Trace Element Data in Pyrite.

Astrobiology
We propose a novel approach to identify the origin of pyrite grains and distinguish biologically influenced sedimentary pyrite using combined sulfur isotope (δS) and trace element (TE) analyses. To classify and predict the origin of individual pyrit...

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions.

Frontiers in immunology
BACKGROUND: Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investiga...

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

PloS one
Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict ...

Machine learning-based new classification for immune infiltration of gliomas.

PloS one
BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effe...

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

PloS one
Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms,...

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment.

Molecular biotechnology
The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laborator...