The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological...
A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is uns...
Circulation. Arrhythmia and electrophysiology
Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low hyperemic coronary flow, which should have different physiological traits and clinical implications....
International journal of neural systems
May 28, 2020
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography...
Neural networks : the official journal of the International Neural Network Society
May 23, 2020
Tracklet association methods learn the cross camera retrieval ability though associating underlying cross camera positive samples, which have proven to be successful in unsupervised person re-identification task. However, most of them use poor-effici...
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Neural networks : the official journal of the International Neural Network Society
May 19, 2020
Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitl...
BACKGROUND: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS.
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we a...
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