Browsing by Author "Kulan, Handan"

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Identification of Critical Proteins Associated With Learning Process for Down Syndrome
(Kadir Has Üniversitesi, 2020) Kulan, Handan; Dağ, Tamer
DS protein profilleri laboratuvarda biyokimyasal teknikler uygulayarak gözlemlenmektedir. Fakat, elde edilen protein listesi uzundur ve listedeki her protein DS ile alakalı değildir. Bu yüzden, DS analizi ve tedavisinde, protein ifade miktarları istatiksel metodlar ve makine öğrenmesi teknikleri uygulayarak analiz edilmektedir. Bu tezde, önceki çalışmalara kıyasla, farklı öndeğerlendirme adımları, özellik seçimi ve sınıﬂandırma teknikleri, farklı veri setleri için protein altkümeleri belirlenmesi için uygulanmıştır. Bu protein altkümeleri fareleri daha doğru şekilde ayrıştırır. Spesifik DS özelliklerinin kritik yolaklara etki eden bu altkümelerdeki proteinler tek tek analiz edildiğinde, seçilmiş proteinlerin öğrenme ve hafıza, sinyal yolakları, Alzheimer hastalığı, bağışıklık sistemi ve hücre ölümü gibi önemli süreçlerde rol aldığı gözlemlenmiştir. Bu tezde seçilen protein alt kümelerinden DS un farklı semptomlarını anlamak için yararlanılabilinir ve DS tedavisinde etkili ilaçlar geliştirmek için kullanılabilinir. The protein proﬁles of people with DS are observed by applying biochemical tech niques in laboratory. However, the list of analyzed proteins is long and not all proteins in list are not related to DS. Thus, for the analysis and the treatment of DS, protein expression levels have been analyzed by applying statistical procedures and machine learning techniques. In this thesis, compared to previous works, different preprocessing steps, feature selection and classiﬁcation techniques are applied to deﬁne the subsets of proteins for datasets. These subsets diﬀerentiate mice more accurately. When these subsets which aﬀect the critical pathways of speciﬁc DS aspects are analyzed, it is monitored that selected proteins have vital roles in the processes, such as apoptosis, learning and memory, signaling pathways, immune sys tem and Alzheimers disease (AD). The subsets of proteins selected in this thesis can be applied to interpret the causes of diﬀerent symptoms in DS and can be utilized to foster eﬀective drugs for the cure of DS.
Citation Count: 0
Importance of Regional Differences in Brain Throughout Aging for Down Syndrome
(Association for Computing Machinery, 2018) Kulan, Handan; Dağ, Tamer
Down syndrome (DS) which affects approximately one in 700 live births is caused by an extra copy of the long arm of human chromosome 21 (HSA21). Statistical analysis has been done for understanding the protein expression profiles based on age and sex differences in DS. In addition there are ongoing research efforts for comprehending expression patterns based on different brain regions. However little is known about the mechanisms of expression differences in brain regions throughout aging. Insights into these mechanisms are required to understand the susceptibility of distinct brain regions to neuronal insults with aging. Dissection of this selective vulnerability will be critical to our understanding of DS. By extracting information from the critical proteins which take part in the mechanism of the molecular pathways the diagnosis of DS can become easier. Also understanding the molecular pathways can contribute to develop effective drugs for the treatment of DS. In this work forward feature selection technique is applied for determining the protein subsets for old and young mice datasets which consist of the expression profiles across different brain regions. When these subsets are analyzed it is observed that selected proteins play important roles in the processes such as mTOR signaling pathway AD MAPK signaling pathway and apoptosis. We believe that the subsets of protein selected in our work can be utilized to understand the process of DS and can be used to develop age-related effective drugs.
Citation Count: 3
In Silico Identification of Critical Proteins Associated With Learning Process and Immune System for Down Syndrome
(Public Library Science, 2019) Kulan, Handan; Dağ, Tamer
Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later deep neural network gradient boosting tree support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system.
Citation Count: 4
Using Machine Learning Classifiers To Identify the Critical Proteins in Down Syndrome
(Association for Computing Machinery, 2018) Kulan, Handan; Dağ, Tamer
Pharmacotherapies of intellectual disability (ID) are largely unknown as the abnormalities at the complex molecular level which causes ID are difficult to understand. Down syndrome (DS) which is the prevalent cause of ID and caused by an extra copy of the human chromosome21 (Hsa21) has been investigated on protein levels by using the Ts65Dn mouse model of DS which are orthologs of %50 of Hsa21 classical protein coding genes. Recent works have applied the classification methods to understand critical factors in DS as it is believed that the problem was naturally related to classification problem since the determination of proteins discriminatory between classes of mice was required. In this study we apply forward feature selection method to identify correlated proteins and their interactions in DS. After identification we report supervised learning model of expression levels of selected proteins in order to understand the critical proteins for diagnosing and explaining DS. The proposed technique depicts optimum classification results achieved by optimizing parameters with grid search. When compared with the former work our classification results give higher accuracy. © 2018 Association for Computing Machinery.