A primer on the use of machine learning to distil knowledge from data in biological psychiatry
Applications of machine learning in the biomedical sciences are growing rapidly. This
growth has been spurred by diverse cross-institutional and interdisciplinary collaborations …
growth has been spurred by diverse cross-institutional and interdisciplinary collaborations …
A new hybrid machine learning for cybersecurity threat detection based on adaptive boosting
P Sornsuwit, S Jaiyen - Applied Artificial Intelligence, 2019 - Taylor & Francis
ABSTRACT A hybrid machine learning is a combination of multiple types of machine
learning algorithms for improving the performance of single classifiers. Currently, cyber …
learning algorithms for improving the performance of single classifiers. Currently, cyber …
Predicting bipolar disorder and schizophrenia based on non-overlapping genetic phenotypes using deep neural network
Computational Psychiatry is an emerging field of science. It focuses on identifying the
complex relationship between the brain's neurobiology. Mental illness has recently become …
complex relationship between the brain's neurobiology. Mental illness has recently become …
The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood
L Zhu, X Wu, B Xu, Z Zhao, J Yang, J Long, L Su - Neuroscience letters, 2021 - Elsevier
Background Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial
disease burden. Machine learning (ML) method can be used to identify individuals with SCZ …
disease burden. Machine learning (ML) method can be used to identify individuals with SCZ …
Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms
Monitoring exposure to xenobiotics by biomarker analyses, such as a micronucleus assay, is
extremely important for the precocious detection and prevention of diseases, such as oral …
extremely important for the precocious detection and prevention of diseases, such as oral …
[PDF][PDF] Signal from noise: Using machine learning to distil knowledge from data in biological psychiatry
Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by
interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic …
interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic …
Transcriptomics and machine learning to advance schizophrenia genetics: a case-control study using post-mortem brain data
B Qi, S Boscenco, J Ramamurthy… - Computer Methods and …, 2022 - Elsevier
Abstract Background and Objective Alterations of the expression of a variety of genes have
been reported in patients with schizophrenia (SCZ). Moreover, machine learning (ML) …
been reported in patients with schizophrenia (SCZ). Moreover, machine learning (ML) …
Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data
DN Purba, F Nhita, I Kurniawan - Jurnal RESTI (Rekayasa Sistem …, 2022 - jurnal.iaii.or.id
Schizophrenia is a chronic mental illness that leads the patient to hallucinations and
delusions with a prevalence of 0.4% worldwide. The importance early detection of …
delusions with a prevalence of 0.4% worldwide. The importance early detection of …
Experimental exposure to gasohol impairs sperm quality with recognition of the classification pattern of exposure groups by machine learning algorithms
KCMT Vieira, AÁ Fernandes, KM Silva… - … Science and Pollution …, 2019 - Springer
Contamination caused by leakage at gas stations leads to possible exposure of the general
population when in contact with contaminated water and soil. The present study aimed to …
population when in contact with contaminated water and soil. The present study aimed to …
Ensemble learning for higher diagnostic precision in schizophrenia using peripheral blood gene expression profile
VV Wagh, T Kottat, S Agrawal, S Purohit… - Neuropsychiatric …, 2024 - Taylor & Francis
Introduction Stigma contributes to a significant part of the burden of schizophrenia (SCZ),
therefore reducing false positives from the diagnosis would be liberating for the individuals …
therefore reducing false positives from the diagnosis would be liberating for the individuals …