Research Outputs

Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2

Browse

Filters

2010 - 201912020 - 20222

Advanced Search

Filter by

Settings

Department: search.filters.department.Department of PhysicsSubject: search.filters.subject.Computational biology

Search Results

Now showing 1 - 3 of 3
  • Placeholder
    PublicationMetadata 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; 49854
    We 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.
  • Placeholder
    PublicationMetadata 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; 49854
    AD 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.
  • Placeholder
    PublicationMetadata only
    DeepCAN: a modular deep learning system for automated cell counting and viability analysis
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Eren, Furkan; Kanarya, Dilek; Aydin, Musa; Kiraz, Berna; Aydin, Omer; N/A; N/A; Department of Physics; Aslan, Mete; Uysallı, Yiğit; Kiraz, Alper; Master Student; PhD Student; Faculty Member; Department of Physics; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Sciences; N/A; N/A; 22542
    Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is error-prone due to operator subjectivity. Recently, deep learning approaches have emerged as powerful tools enabling quick and accurate image cytometry applicable to different cell types. Leading to simpler, compact, and affordable solutions, these approaches revealed image cytometry as a viable alternative to flow cytometry or Coulter counting. In this study, we demonstrate a modular deep learning system, DeepCAN, providing a complete solution for automated cell counting and viability analysis. DeepCAN employs three different neural network blocks called Parallel Segmenter, Cluster CNN, and Viability CNN that are trained for initial segmentation, cluster separation, and viability analysis. Parallel Segmenter and Cluster CNN blocks achieve accurate segmentation of individual cells while Viability CNN block performs viability classification. A modified U-Net network, a well-known deep neural network model for bioimage analysis, is used in Parallel Segmenter while LeNet-5 architecture and its modified version Opto-Net are used for Cluster CNN and Viability CNN, respectively. We train the Parallel Segmenter using 15 images of A2780 cells and 5 images of yeasts cells, containing, in total, 14742 individual cell images. Similarly, 6101 and 5900 A2780 cell images are employed for training Cluster CNN and Viability CNN models, respectively. 2514 individual A2780 cell images are used to test the overall segmentation performance of Parallel Segmenter combined with Cluster CNN, revealing high Precision/Recall/F1-Score values of 96.52%/96.45%/98.06%, respectively. Cell counting/viability performance of DeepCAN is tested with A2780 (2514 cells), A549 (601 cells), Colo (356 cells), and MDA-MB-231 (887 cells) cell images revealing high analysis accuracies of 96.76%/99.02%, 93.82%/95.93%, and 92.18%/97.90%, 85.32%/97.40%, respectively.