AIMC Topic: Reproducibility of Results

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Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like comput...

Enhancing analysis throughput, sensitivity and specificity in LC/ESI-MS/MS assay of plasma 25-hydroxyvitamin D by derivatization with triplex 4-(4-dimethylaminophenyl)-1,2,4-triazoline-3,5-dione (DAPTAD) isotopologues.

Journal of pharmaceutical and biomedical analysis
The plasma/serum concentration of 25-hydroxyvitamin D [25(OH)D] is a diagnostic index for vitamin D deficiency/insufficiency, which is associated with a wide range of diseases, such as rickets, cancer and diabetes. We have reported that the derivatiz...

Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping.

IEEE transactions on bio-medical engineering
GOAL: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions.

Segmentation of Fetal Left Ventricle in Echocardiographic Sequences Based on Dynamic Convolutional Neural Networks.

IEEE transactions on bio-medical engineering
Segmentation of fetal left ventricle (LV) in echocardiographic sequences is important for further quantitative analysis of fetal cardiac function. However, image gross inhomogeneities and fetal random movements make the segmentation a challenging pro...

First report of robot-assisted transperineal fusion versus off-target biopsy in patients undergoing repeat prostate biopsy.

World journal of urology
PURPOSE: To clarify the value of targeted versus off-target biopsies in men with a suspicion of prostate cancer (PC) and a visible lesion in multi-parametric magnetic resonance imaging (mpMRI) using transperineal robot-assisted biopsy.

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.

BMC genomics
BACKGROUND: Next-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by t...

Preschoolers Flexibly Adapt to Linguistic Input in a Noisy Channel.

Psychological science
Because linguistic communication is inherently noisy and uncertain, adult language comprehenders integrate bottom-up cues from speech perception with top-down expectations about what speakers are likely to say. Further, in line with the predictions o...

The Variability of Translocator Protein Signal in Brain and Blood of Genotyped Healthy Humans Using In Vivo I-CLINDE SPECT Imaging: A Test-Retest Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
I-CLINDE is a radiotracer developed for SPECT and targets the 18-kDa translocator protein (TSPO). TSPO is upregulated in glial cells and used as a measure of neuroinflammation in a variety of central nervous system diseases. The aim of this study was...

Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality.

PloS one
Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a ...

Using machine learning to parse breast pathology reports.

Breast cancer research and treatment
PURPOSE: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine le...