AIMC Topic: Machine Learning

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Machine Learning Improves Risk Stratification After Acute Coronary Syndrome.

Scientific reports
The accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms...

Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion.

IEEE transactions on neural networks and learning systems
While non-negative blind source separation (nBSS) has found many successful applications in science and engineering, model order selection, determining the number of sources, remains a critical yet unresolved problem. Various model order selection me...

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

World neurosurgery
OBJECTIVE: Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the interven...

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

BMC bioinformatics
BACKGROUND: One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to le...

Using machine learning algorithms to identify genes essential for cell survival.

BMC bioinformatics
BACKGROUND: With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive....

Patchy promiscuity: machine learning applied to predict the host specificity of and .

Microbial genomics
and are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reserv...

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted ...

(Machine-)Learning to analyze in vivo microscopy: Support vector machines.

Biochimica et biophysica acta. Proteins and proteomics
The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and subcellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches p...

Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.

BMC bioinformatics
BACKGROUND: Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms...

Improved protein contact predictions with the MetaPSICOV2 server in CASP12.

Proteins
In this paper, we present the results for the MetaPSICOV2 contact prediction server in the CASP12 community experiment (http://predictioncenter.org). Over the 35 assessed Free Modelling target domains the MetaPSICOV2 server achieved a mean precision ...