AIMC Topic: Machine Learning

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How Cognitive Machines Can Augment Medical Imaging.

AJR. American journal of roentgenology
OBJECTIVE: Artificial intelligence (AI) neural networks rapidly convert disparate facts and data into highly predictive analytic models. Machine learning maps image-patient phenotype correlations opaque to standard statistics. Deep learning performs ...

Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter.

Journal of medicinal chemistry
Assay interference compounds give rise to false-positives and cause substantial problems in medicinal chemistry. Nearly 500 compound classes have been designated as pan-assay interference compounds (PAINS), which typically occur as substructures in o...

Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program.

Molecules (Basel, Switzerland)
The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the pr...

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.

Scientific reports
Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have fac...

Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort.

PLoS medicine
BACKGROUND: The relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We propose that the discrepancies arise in part because allergic sensitisation may not be a single ent...

Transforming health policy through machine learning.

PLoS medicine
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Magnetic resonance in medicine
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response t...

The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review.

Journal of affective disorders
BACKGROUND: Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thought...

Systems Biology and Machine Learning in Plant-Pathogen Interactions.

Molecular plant-microbe interactions : MPMI
Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abu...