Browsing by Author "Fakhouri, Mohamad"

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    Bardezag orphanage
    (Bilkent University, 2018) Khan, Muhammad Arham; Khalid, Muhammad Bilal Bin; Fakhouri, Mohamad; Aftab, Abdül Moiz; Elahi, Sepehr
    Bu makalede, Bardezag Yetimhanesi’nin İzmit bölgesinde açılmasına neden olan Ermeni ve Osmanlı Devleti arasında gerçekleşen çatışmalar ve Ermeni ailelerin çocuklarını yetim ve güçsüz bırakan olaylar incelenmiştir. Bu olayların başta gelenlerinden bazıları Ermenilerin toplu olarak katledilmesi, bölgede yaşayan Ermenilerin ödediği vergilerin ikiye çıkarılması ve Türklerin Ermenilere herhangi bir yardım ve destekte bulunmaması olmuştur. Yetimhanenin kurulmasını sağlayan nedenler tartışıldıktan sonra yetimhaneye yabancı ülkeler tarafından yapılan bağışların hızla artan yetim sayısına yetmemesinin ardından giderlerini hangi yollarla karşıladığı incelenmiş ve çocuklar arasında iş bölümü yaparak giderlerini en aza indirdiği görülmüştür.
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    Uncovering complementary sets of variants for predicting quantitative phenotypes
    (Oxford University Press, 2021-12-02) Yılmaz, S.; Fakhouri, Mohamad; Koyutürk, M.; Çiçek, A. E.; Taştan, Ö.
    Motivation: Genome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning. Results: We propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are likely to be in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-ofthe-art selection methods while having a simpler premise and being at least two orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with 107 variants in a matter of minutes while taking the dependencies between the variants into account. Availabilityand implementation: Macarons is available in Matlab and Python at https://github.com/serhan-yilmaz/macarons.