AIMC Topic: Machine Learning

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Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.

Journal of Alzheimer's disease : JAD
BACKGROUND: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET ima...

Computational Models for Self-Interacting Proteins Prediction.

Protein and peptide letters
Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to un...

Sustainable level of human performance with regard to actual availability in different professions.

Work (Reading, Mass.)
BACKGROUND: In a real working environment, workers' performance depends on the level of competence, psychological and health condition, motivation, and perceived stress. These are the attributes of actual availability. It is crucial to identify the m...

Statistical and Machine Learning Methods for eQTL Analysis.

Methods in molecular biology (Clifton, N.J.)
An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regula...

Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

Journal of consulting and clinical psychology
OBJECTIVE: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of st...

Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach.

Journal of consulting and clinical psychology
OBJECTIVE: Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available, but there is a lack of evidence to decide which treatment works best for whom. This study aimed to ...

Machine Learning Principles for Radiology Investigators.

Academic radiology
Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary ...

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

[A primer on machine learning].

Der Radiologe
BACKGROUND: The methods of machine learning and artificial intelligence are slowly but surely being introduced in everyday medical practice. In the future, they will support us in diagnosis and therapy and thus improve treatment for the benefit of th...