@inproceedings{a6bef9a072fd47d8a2d1c868da3fbd3a,
title = "Analysis of Bipolar Disorder Genomic Data to Identify Differences in Gene Expression",
abstract = "The main focus of the paper is the problem of correct diagnosis of bipolar disorder based on differences in gene expression present in peripheral blood leukocytes. The work was aimed at finding biomarkers that could facilitate the process of diagnosing bipolar disorder. For this purpose, data obtained from Affymetrix microarrays were analyzed. The methodology of the work involved preprocessing of the microarray data obtained from 54 patients and then performing a statistical analysis. The obtained statistically significant genes were described and compared with literature sources. The next step included hierarchical clustering and the k-means clustering methods, as well as classification using the support vector machine and linear discriminant analysis. Research has shown that most of the identified genes have been linked to bipolar disorder in literature sources. The genes that were extracted were able to distinguish the samples belonging to the control group and the bipolar disorder group with satisfying results.",
keywords = "Bipolar disorder, Classification, Cluster Analysis, Gene Expression, Microarrays",
author = "Anna Tamulewicz and Victoria Budziak",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 21st Scientific Conference on Medical and Sport Technologies, 2024 ; Conference date: 17-05-2024 Through 19-05-2024",
year = "2025",
doi = "10.1007/978-3-031-82143-1\_18",
language = "English",
isbn = "9783031821424",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "161--168",
editor = "Marek Gzik and Ewa Pi{\c e}tka and Jacek Jurkoj{\'c} and Zbigniew Paszenda and Krzysztof Milewski",
booktitle = "Innovations in Biomedical Engineering 2024",
address = "Germany",
}