Schizophrenia

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be us...

Intuitionistic Fuzzy Weighted Least Squares Twin SVMs.

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuit...

The Association Between Clozapine Plasma Concentration, CYP2D6 (*10, *2) Polymorphisms and Risk of Adverse Reactions.

BACKGROUND: The aim of this article was to study the relationships between the risk of adverse reactions, plasma concentration, and cytochrome P450 2D...

MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data.

Artificial intelligence-based models and robust computational methods have expedited the data-to-knowledge trajectory in precision medicine. Although ...

Generative artificial intelligence: Can ChatGPT write a quality abstract?

ChatGPT is a generative artificial intelligence chatbot which may have a role in medicine and science. We investigated if the freely available version...

Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.

Scientific reproducibility that effectively leverages existing study data is critical to the advancement of research in many disciplines including neu...

Artificial hallucination: GPT on LSD?

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based pri...

Automated detection of schizophrenia using deep learning: a review for the last decade.

Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the...

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease.

The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the l...

Android robot was beneficial for communication rehabilitation in a patient with schizophrenia comorbid with autism spectrum disorders.

De novo design of luciferases using deep learning.

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into ge...

Computing personalized brain functional networks from fMRI using self-supervised deep learning.

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functi...

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data.

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gai...

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.

PURPOSE OF REVIEW: This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psyc...

Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.

Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the presen...

A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data.

Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network conne...

Noise Robust Face Hallucination Based on Smooth Correntropy Representation.

Face hallucination technologies have been widely developed during the past decades, among which the sparse manifold learning (SML)-based approaches ha...

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis.

Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample si...

Hallucinating symmetric protein assemblies.

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural p...