Journal article

Downregulated transferrin receptor in the blood predicts recurrent MDD in the elderly cohort: A fuzzy forests approach

LG Ciobanu, PS Sachdev, JN Trollor, S Reppermund, A Thalamuthu, KA Mather, S Cohen-Woods, D Stacey, C Toben, KO Schubert, BT Baune

Journal of Affective Disorders | ELSEVIER | Published : 2020

Abstract

Background: At present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations. Methods: We aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that take..

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University of Melbourne Researchers

Grants

Awarded by Flinders University


Funding Acknowledgements

This work was supported by funding from the National Health and Medical Research Council (NHMRC; ID 1060524 to BTB, SCW, SR, JT) of Australia. The Sydney Memory and Ageing Study (SMAS) was supported by a National Health and Medical Research Council (NHMRC)/Australian Research Council Strategic Award (ID 401162), NHMRC Program Grants (ID 350833 and 568969) and a Project Grant (ID 1045325). The Older Australian Twins Study (OATS) was funded by an NHMRC/ARC Strategic Award Grant of the Ageing Well, Ageing Productively Program (ID 401162) and NHMRC Project Grant (ID 1045325 and 1085606). SCW is supported by the Matthew Flinders Fellowship, Flinders University, Australia.