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Publication Metadata only A Bayesian generalized linear model for Crimean–Congo hemorrhagic fever incidents(Springer, 2018) Ryu, Duchwan; Bilgili, Devrim; Liang, Faming; Ebrahimi, Nader; Ergönül, Önder; Faculty Member; School of Medicine; 110398Global spread of the Crimean-Congo hemorrhagic fever (CCHF) is a fatal viral infection disease found in parts of Africa, Asia, Eastern Europe and Middle East, with a fatality rate of up to 30%. A timely prediction of the prevalence of CCHF incidents is highly desirable, while CCHF incidents often exhibit nonlinearity in both temporal and spatial features. However, the modeling of discrete incidents is not trivial. Moreover, the CCHF incidents are monthly observed in a long period and take a nonlinear pattern over a region at each time point. Hence, the estimation and the data assimilation for incidents require extensive computations. In this paper, using the data augmentation with latent variables, we propose to utilize a dynamically weighted particle filter to take advantage of its population controlling feature in data assimilation. We apply our approach in an analysis of monthly CCHF incidents data collected in Turkey between 2004 and 2012. The results indicate that CCHF incidents are higher at Northern Central Turkey during summer and that some beforehand interventions to stop the propagation are recommendable. Supplementary materials accompanying this paper appear on-line.Publication Restricted A data-centric approach for investigation of protein-protein interfaces in protein data bank(Koç University, 2021) Abalı, Zeynep; Keskin, Özlem; 0000-0002-4202-4049; Koç University Graduate School of Sciences and Engineering; Data Science; 26605Publication Metadata only Analysis of cortical morphometric variability using labeled cortical distance maps(Int Press Boston, Inc, 2017) Nishino, T.; Botteron, K. N.; Miller, M. I.; Ratnanather, J. T.; Department of Mathematics; Ceyhan, Elvan; Faculty Member; Department of Mathematics; College of Sciences; N/AMorphometric (i.e., shape and size) differences in the anatomy of cortical structures are associated with neuro-developmental and neuropsychiatric disorders. Such differences can be quantized and detected by a powerful tool called Labeled Cortical Distance Map (LCDM). The LCDM method provides distances of labeled gray matter (GM) voxels from the GM/white matter (WM) surface for specific cortical structures (or tissues). Here we describe a method to analyze morphometric variability in the particular tissue using LCDM distances. To extract more of the information provided by LCDM distances, we perform pooling and censoring of LCDM distances. In particular, we employ Brown-Forsythe (BF) test of homogeneity of variance (HOV) on the LCDM distances. HOV analysis of pooled distances provides an overall analysis of morphometric variability of the LCDMs due to the disease in question, while the HOV analysis of censored distances suggests the location(s) of significant variation in these differences (i.e., at which distance from the GM/WM surface the morphometric variability starts to be significant). We also check for the influence of assumption violations on the HOV analysis of LCDM distances. In particular, we demonstrate that BF HOV test is robust to assumption violations such as the non-normality and within sample dependence of the residuals from the median for pooled and censored distances and are robust to data aggregation which occurs in analysis of censored distances. We recommend HOV analysis as a complementary tool to the analysis of distribution/location differences. We also apply the methodology on simulated normal and exponential data sets and assess the performance of the methods when more of the underlying assumptions are satisfied. We illustrate the methodology on a real data example, namely, LCDM distances of GM voxels in ventral medial prefrontal cortices (VMPFCs) to see the effects of depression or being of high risk to depression on the morphometry of VMPFCs. The methodology used here is also valid for morphometric analysis of other cortical structures.Publication Restricted Analysis of human protein-protein interaction network and cancer proteins using structural information(Koç University, 2008) Kar, Gözde; Keskin, Özlem; 0000-0002-4202-4049; Koç University Graduate School of Sciences and Engineering; Electrical and Computer Engineering; 26605Publication Metadata only Anomalies in the transcriptional regulatory network of the Yeast Saccharomyces cerevisiae(Elsevier, 2010) N/A; Department of Physics; Tuğrul, Murat; Kabakçıoğlu, Alkan; N/A; Faculty Member; Department of Physics; Graduate School of Sciences and Engineering; College of Sciences; N/A; 49854We investigate the structural and dynamical properties of the transcriptional regulatory network of the Yeast Saccharomyces cerevisiae and compare it with two "unbiased" ensembles: one obtained by reshuffling the edges and the other generated by mimicking the transcriptional regulation mechanism within the cell. Both ensembles reproduce the degree distributions (the first-by construction-exactly and the second approximately), degree-degree correlations and the k-core structure observed in Yeast. An exceptionally large dynamically relevant core network found in Yeast in comparison with the second ensemble points to a strong bias towards a collective organization which is achieved by subtle modifications in the network's degree distributions. We use a Boolean model of regulatory dynamics with various classes of update functions to represent in vivo regulatory interactions. We find that the Yeast's core network has a qualitatively different behavior, accommodating on average multiple attractors unlike typical members of both reference ensembles which converge to a single dominant attractor. Finally, we investigate the robustness of the networks and find that the stability depends strongly on the used function class. The robustness measure is squeezed into a narrower band around the order-chaos boundary when Boolean inputs are required to be nonredundant on each node. However, the difference between the reference models and the Yeast's core is marginal, suggesting that the dynamically stable network elements are located mostly on the peripherals of the regulatory network. Consistently, the statistically significant three-node motifs in the dynamical core of Yeast turn out to be different from and less stable than those found in the full transcriptional regulatory network.Publication Metadata only Audio-driven human body motion analysis and synthesis(IEEE, 2008) Canton-Ferrer, C.; Tilmanne, J.; Bozkurt, E.; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Ofli, Ferda; Demir, Yasemin; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 107907; 34503; 26207This paper presents a framework for audio-driven human body motion analysis and synthesis. We address the problem in the context of a dance performance, where gestures and movements of the dancer are mainly driven by a musical piece and characterized by the repetition of a set of dance figures. The system is trained in a supervised manner using the multiview video recordings of the dancer. The human body posture is extracted from multiview video information without any human intervention using a novel marker-based algorithm based on annealing particle filtering. Audio is analyzed to extract beat and tempo information. The joint analysis of audio and motion features provides a correlation model that is then used to animate a dancing avatar when driven with any musical piece of the same genre. Results are provided showing the effectiveness of the proposed algorithm.Publication Metadata only B-tensor: brain connectome tensor factorization for Alzheimer's disease(Institute of Electrical and Electronics Engineers (IEEE), 2021) Durusoy, Goktekin; Yildirm, Zerrin; Dal, Demet Yuksel; Ulasoglu-Yildiz, Cigdem; Kurt, Elif; Bayir, Gunes; Ozacar, Erhan; Ozarslan, Evren; Demirtas-Tatldede, Asl; Bilgic, Basar; Demiralp, Tamer; Gurvit, Hakan; Acar, Burak; Department of Physics; Kabakçıoğlu, Alkan; Faculty Member; Department of Physics; College of Sciences; 49854AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e., sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes to define a low-dimensional space via tensor factorization. We show on a cohort of 47 subjects, spanning the spectrum of dementia, that diagnosis with an accuracy of 77% to 100% is achievable in a 5D connectome space using different structural and functional connectome constructions in a uni-modal and multi-modal fashion. We further show that multi-modal tensor factorization improves the results suggesting complementary information in structure and function. A neurological assessment of the connectivity patterns identified largely agrees with prior knowledge, yet also suggests new associations that may play a role in the disease progress.Publication Metadata only Biologically inspired dynamic spectrum access in cognitive radio networks(CRC Press, 2016) N/A; Department of Electrical and Electronics Engineering; Atakan, Barış; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647N/APublication Restricted Computational search of the interaction between melanopsin & cryptochrome proteins(Koç University, 2006) Ünal, Evrim Besray; Erman, Burak; Kavaklı, İbrahim Halil; 0000-0002-2496-6059; 0000-0001-6624-3505; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 179997; 40319Publication Restricted Context-specific signaling pathway construction in cancer through network motif search(Koç University, 2023) Uzun, Ceren; Tunçbağ, Nurcan; 0000-0002-0389-9459; Koç University Graduate School of Sciences and Engineering; Computational Sciences and Engineering; 245513