AIMC Topic: Machine Learning

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Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model.

International journal of radiation oncology, biology, physics
PURPOSE: To determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival (BMETS) to predict survival using 27 prognostic ...

Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.

Computer methods and programs in biomedicine
BACKGROUND: Intradialytic hypotension (IDH) is commonly occurred and links to higher mortality among patients undergoing hemodialysis (HD). Its early prediction and prevention will dramatically improve the quality of life. However, predicting the occ...

The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.

International journal of computer assisted radiology and surgery
PURPOSE: The manual generation of training data for the semantic segmentation of medical images using deep neural networks is a time-consuming and error-prone task. In this paper, we investigate the effect of different levels of realism on the traini...

Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning.

Chemical communications (Cambridge, England)
Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify "hotspots" have re...

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.

Scientific reports
Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the predi...

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Blood cancer journal
Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in ma...

A biochemically-interpretable machine learning classifier for microbial GWAS.

Nature communications
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machi...

Health status prediction for the elderly based on machine learning.

Archives of gerontology and geriatrics
Health and social care services are crucial to old people. The provision of services to the elderly with care needs requires more accurate predictions of the health status of the elderly to rationalize the allocation of the limited social care resour...

Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined.

Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.

Analytical chemistry
Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Tw...