Osman Ugur Sezerman

Abstract Predicting disease-associated peptides is a challenging task in bioinformatics, mostly hindered by the lack of reliable negative datasets, leading to biased predictions. In this study, we propose a one-class classification approach that focuses exclusively on positive-labeled data. We employed three classifiers namely One-Class Support Vector Machines (OCSVM), Isolation Forest, and Autoencoders to classify disease-associated peptides, with Autoencoders yielding the best results.

Hydroxyprogesterone (HP) is a synthetic progestogen widely used in obstetric care, and its potential influence on breast cancer biology has become an emerging area of interest. Despite its clinical use, the molecular mechanisms by which HP affects tumor tissue remain insufficiently explored. In this study, transcriptomic profiling was performed to investigate gene expression changes associated with HP in operable breast cancer.

BAR19 Brucella abortus S19 reference strain BAS19 Brucella abortus S19 field strain bp base pairs CDS coding DNA sequence EC Enzyme Commission FAST5 raw signal data format for nanopore sequencing FASTQC fast quality control GC Content guanine–cytosine content INDEL insertion and deletion KEGG Kyoto Encyclopaedia of Genes and Genomes L50 contig count at which 50% of the genome is covered MDS molecular dynamics simulation BoLA Bovine Leukocyte Antigen BoLA I/II Bovine Leukocyte Antigen I/II N50...