Author

Monika S. Mellem

ORCID 0000-0002-1687-5683Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:machine learning (classical)· 3 Assay:imaging· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Parvez AhammadiD· 3 shared Yuelu Liu· 3 shared Humberto GonzáleziD· 2 shared Matthew KolladaiD· 2 shared William J. MartiniD· 2 shared Annette Madrid· 1 shared Atul R. Mahableshwarkar· 1 shared Diego A. PizzagalliiD· 1 shared

Activity by Year

2018-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersNeuroscience

Parvez AhammadORCID

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Author profile →

3 papers

Recent·Most cited

Machine learning identifies large-scale reward-related activity modulated by dopaminergic enhancement in major depression

Yuelu Liu, Roee Admon, Monika S. Mellem, Emily L. Belleau, Roselinde H. Kaiser, Rachel A. Clegg, Miranda Beltzer, Franziska Goer, Gordana D. Vitaliano, Parvez Ahammad, Diego A. Pizzagalli

The paper uses machine learning to analyze fMRI data and identifies reward-related brain activity changes in MDD patients after dopaminergic enhancement.

Biological Psychiatry Cognitive Neuroscience and Neuroimaging · Oct 22, 2019Peer-reviewedNeuroscience

Machine learning models identify multimodal measurements highly predictive of transdiagnostic symptom severity for mood, anhedonia, and anxiety

Monika S. Mellem, Yuelu Liu, Humberto González, Matthew Kollada, William J. Martin, Parvez Ahammad

The paper develops machine learning models using multimodal data to predict transdiagnostic psychiatric symptom severity, achieving high variance explanation and revealing key predictors.

Biological Psychiatry Cognitive Neuroscience and Neuroimaging · Jul 30, 2019Peer-reviewedNeuroscience

Highly Predictive Transdiagnostic Features Shared across Schizophrenia, Bipolar Disorder, and ADHD Identified Using a Machine Learning Based Approach

Yuelu Liu, Monika S. Mellem, Humberto González, Matthew Kollada, Atul R. Mahableshwarkar, Annette Madrid, William J. Martin, Parvez Ahammad

The paper identifies transdiagnostic features shared across schizophrenia, bipolar disorder, and ADHD using machine learning on self-reported data, showing high predictive power for distinguishing patients from controls.

bioRxiv · Dec 18, 2018 · PreprintNeuroscience