AIMC Topic: Multivariate Analysis

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Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.

Developmental science
Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we ap...

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks.

PloS one
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, a...

Anti-Müllerian hormone concentrations and antral follicle counts for the prediction of pregnancy outcomes after intrauterine insemination.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To evaluate anti-Müllerian hormone (AMH) concentrations and antral follicle counts (AFCs) in the prediction of pregnancy outcomes after controlled ovarian stimulation among women undergoing intrauterine insemination.

The relationship of blood transfusion with peri-operative and long-term outcomes after major hepatectomy for metastatic colorectal cancer: a multi-institutional study of 456 patients.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Data on prognostic implications of peri-operative blood transfusion around resection of colorectal cancer liver metastases (CRLM) are conflicting. This retrospective study assesses the association of transfusion with complications and dis...

Pregnancy risk factors in autism: a pilot study with artificial neural networks.

Pediatric research
BACKGROUND: Autism is a multifactorial condition in which a single risk factor can unlikely provide comprehensive explanation for the disease origin. Moreover, due to the complexity of risk factors interplay, traditional statistics is often unable to...

Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.

The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

Waste management (New York, N.Y.)
Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verifi...