AIMC Topic: Machine Learning

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Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.

American journal of epidemiology
In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficien...

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.

Journal of digital imaging
Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and lo...

Machine learning in laboratory medicine: waiting for the flood?

Clinical chemistry and laboratory medicine
This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to ...

Image reconstruction by domain-transform manifold learning.

Nature
Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio a...

[A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after prebiotics consumption].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To investigate the effects of prebiotics supplementation for 9 days on gut microbiota structure and function and establish a machine learning model based on the initial gut microbiota data for predicting the variation of Bifidobacterium af...

Research progress in machine learning methods for gene-gene interaction detection.

Yi chuan = Hereditas
Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to...

Identification of topological features in renal tumor microenvironment associated with patient survival.

Bioinformatics (Oxford, England)
MOTIVATION: As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/...

Machine learning annotation of human branchpoints.

Bioinformatics (Oxford, England)
MOTIVATION: The branchpoint element is required for the first lariat-forming reaction in splicing. However current catalogues of human branchpoints remain incomplete due to the difficulty in experimentally identifying these splicing elements. To addr...