Bipolar disorder (BD) presents with a progressive course in a subset of patients. However,
our knowledge of molecular changes in older BD is limited. In this study, we examined gene …

Introduction Advances in machine learning (ML) methodologies, combined with
multidisciplinary collaborations across biological and physical sciences, has the potential to …

Aim: Many small datasets of significant value exist in the medical space that are being
underutilized. Due to the heterogeneity of complex disorders found in oncology, systems …

The development of sequencing technology has promoted the expansion of cancer genome
data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore …

Background Transcriptomic profile differences between patients with bipolar disorder and
healthy controls can be identified using machine learning and can provide information about …

Nurosene's NURO app (nurosene. com) is an innovative smartphone application that
gathers and analyzes active self-report metrics from users, empowering them with data …

Importance: Examining translatable mitochondrial blood-based biological markers to identify
its association with metabolic diseases in bipolar disorder. Objective: To test whether …

Objective Complex machine learning classification algorithms using transcriptome data from
post-mortem cerebellar tissue of bipolar patients and unaffected controls, have been …

Öz Ruh sağlığı alanında objektif tanısal değerlendirme ve etkili müdahalelerin
geliştirilmesine yardımcı olabilecek fenomenleri takip etmek ve bu alana hem fiziksel hem …

Digital health technologies (DHTs) are defined as systems that use computing platforms,
connectivity, software, and sensors for healthcare and related purposes. These technologies …