Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.

Background: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hidden systemic biases such as GC bias, which can significantly affect the sensitivity and specificity of CNV detection. In many cases, the kit manifests provide only the genome coordinates of the targeted regions, and the exact bait design of the oligo capture baits is not available.

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder.

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer.

Automatic segmentation of hyperreflective foci in OCT images.

Background And Objective: The leading cause of vision loss in the Western World is Age-related Macular Degeneration (AMD), but together with modern medicines, tracking the number of Hyperreflective Foci (HF) on Optical Coherence Tomography (OCT) images should assist the treatment of patients. Here, we developed a framework based on deep learning for the automatic segmentation of HF in OCT images.

Methods: We collected OCT images and annotated them, then these images underwent image preprocessing, and feature extraction steps.

Two subgroups of schizophrenia identified by systematic cognitive neuropsychiatric mapping.

The description of the heterogeneous phenomenological, pathophysiological, and etiological nature of schizophrenia is under way; however, the relationships between heterogeneity levels are still unclear. We performed a robust cross-sectional study, including a systematic neuropsychological battery, assessment of clinical symptoms, neurological soft signs, morphogenetic anomalies and smell identification, and measurement of event-related potentials on 50 outpatients with schizophrenia in their compensated states. An explorative fuzzy cluster analysis revealed two subgroups in this sample that could be distinguished from each other on symptomatological, cognitive and neurological levels.

Applying fuzzy technologies to equivalence learning in protein classification.

When sequencing a new genome, its function and structure are important concerns, and inferring methods are based on protein sequence similarity methods. However, sequence groups differ in their parameters such as the number of group members and intra- and inter-class variability. A method that performs well on one group may not perform well on another group.