Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

2 Dec 2019

Abstract excerpt

OBJECTIVE: This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls. METHODS: We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Bipolar Disorder and Treatment
Share to Mental Health Research Topics
Share to Tryptophan and brain disorders

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 31789053
DOI: 10.1177/0004867419888027

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approachPMID 31789053Read ↗

Proteomic approaches to identify blood-based biomarkers for depression and bipolar disordersSemantic proximity Read ↗

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning MethodsSemantic proximity Read ↗

Identifying circulating biomarkers for major depressive disorderSemantic proximity Read ↗

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltrationSemantic proximity Read ↗

Increased depression genetic liability associates with inflammatory markers in multiple electronic health record systemsSemantic proximity Read ↗

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Disease ChronicitySemantic proximity Read ↗

A diagnostic model based on bioinformatics and machine learning to differentiate bipolar disorder from schizophrenia and major depressive disorderSemantic proximity Read ↗

Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depressionSemantic proximity Read ↗

<span class="word">Peripheral <span class="word">Inflammatory <span class="word">Biomarkers <span class="word">in <span class="word">First-<span class="word"><span class="changedDisabled">Episode, <span class="word">Drug-<span class="word"><span class="changedDisabled">Naïve <span class="word">Major <span class="word">Depressive <span class="word">Disorder: <span class="word">A <span class="word">Systematic <span class="word">ReviewSemantic proximity Read ↗

Biomarkers predicting treatment outcome in depression: what is clinically significant?Semantic proximity Read ↗

A review of new insights into existing major depressive disorder biomarkersSemantic proximity Read ↗

Major depressive disorder: Biomarkers and biosensorsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.