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Yayın Design and analysis of classifier learning experiments in bioinformatics: survey and case studies(IEEE Computer Soc, 2012-12) İrsoy, Ozan; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim EthemIn many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design of such experiments and the analysis of the resulting data using statistical tests should be done carefully for the results to carry significance. In this paper, we first review the performance measures used in classification, the basics of experiment design and statistical tests. We then give the results of our survey over 1,500 papers published in the last two years in three bioinformatics journals (including this one). Although the basics of experiment design are well understood, such as resampling instead of using a single training set and the use of different performance metrics instead of error, only 21 percent of the papers use any statistical test for comparison. In the third part, we analyze four different scenarios which we encounter frequently in the bioinformatics literature, discussing the proper statistical methodology as well as showing an example case study for each. With the supplementary software, we hope that the guidelines we discuss will play an important role in future studies.Yayın White-matter changes in early and late stages of mild cognitive impairment(Churchill Livingstone, 2020-08) Femir Gürtuna, Banu; Kurt, Elif; Ulaşoğlu Yıldız, Çiğdem; Bayram, Ali; Yıldırım, Elif; Soncu Büyükişcan, Ezgi; Bilgiç, BaşarMild Cognitive Impairment (MCI) is characterized by cognitive deficits that exceed age-related decline, but not interfering with daily living activities. Amnestic type of the disorder (aMCI) is known to have a high risk to progress to Alzheimer's Disease (AD), the most common type of dementia. Identification of very early structural changes in the brain related to the cognitive decline in MCI patients would further contribute to the understanding of the dementias. In the current study, we target to investigate whether the white-matter changes are related to structural changes, as well as the cognitive performance of MCI patients. Forty-nine MCI patients were classified as Early MCI (E-MCI, n = 24) and Late MCI (L-MCI, n = 25) due to their performance on The Free and Cued Selective Reminding Test (FCSRT). Age-Related White-Matter Changes (ARWMC) scale was used to evaluate the white-matter changes in the brain. Volumes of specific brain regions were calculated with the FreeSurfer program. Both group and correlation analyses were conducted to show if there was any association between white-matter hyperintensities (WMHs) and structural changes and cognitive performance. Our results indicate that, L-MCI patients had significantly more WMHs not in all but only in the frontal regions compared to E-MCI patients. Besides, ARWMC scores were not correlated with total hippocampal and white-matter volumes. It can be concluded that WMHs play an important role in MCI and cognitive functions are affected by white-matter changes of MCI patients, especially in the frontal regions.Yayın Theta and Beta1 frequency band values predict dyslexia classification(John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.
