PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/4466

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Now showing 1 - 16 of 16

Citation Count: 11
Accurate Refinement Of Docked Protein Complexes Using Evolutionary Information And Deep Learning
(Imperıal College Press, 2016) Akbal-Delibas, Bahar; Farhoodi, Roshanak; Pomplun, Marc; Haspel, Nurit
One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work we introduced AccuRefiner a machine learning based tool for refining protein-protein complexes. Given a docked complex the refinement tool produces a small set of refined versions of the input complex with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 angstrom error margin on average by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.
Citation Count: 54
BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks
(Oxford University Press, 2014) Erten, Cesim; Erten, Cesim
Motivation: Global many-to-many alignment of biological networks has been a central problem in comparative biological network studies. Given a set of biological interaction networks the informal goal is to group together related nodes. For the case of protein-protein interaction networks such groups are expected to form clusters of functionally orthologous proteins. Construction of such clusters for networks from different species may prove useful in determining evolutionary relationships in predicting the functions of proteins with unknown functions and in verifying those with estimated functions. Results: A central informal objective in constructing clusters of orthologous proteins is to guarantee that each cluster is composed of members with high homological similarity usually determined via sequence similarities and that the interactions of the proteins involved in the same cluster are conserved across the input networks. We provide a formal definition of the global many-to-many alignment of multiple protein-protein interaction networks that captures this informal objective. We show the computational intractability of the suggested definition. We provide a heuristic method based on backbone extraction and merge strategy (BEAMS) for the problem. We finally show through experiments based on biological significance tests that the proposed BEAMS algorithm performs better than the state-of-the-art approaches. Furthermore the computational burden of the BEAMS algorithm in terms of execution speed and memory requirements is more reasonable than the competing algorithms.
Citation Count: 14
CAMPways: constrained alignment framework for the comparative analysis of a pair of metabolic pathways
(Oxford University Press, 2013) Erten, Cesim; Biyikoglu, Turker; Erten, Cesim
Motivation: Given a pair of metabolic pathways an alignment of the pathways corresponds to a mapping between similar substructures of the pair. Successful alignments may provide useful applications in phylogenetic tree reconstruction drug design and overall may enhance our understanding of cellular metabolism. Results: We consider the problem of providing one-to-many alignments of reactions in a pair of metabolic pathways. We first provide a constrained alignment framework applicable to the problem. We show that the constrained alignment problem even in a primitive setting is computationally intractable which justifies efforts for designing efficient heuristics. We present our Constrained Alignment of Metabolic Pathways (CAMPways) algorithm designed for this purpose. Through extensive experiments involving a large pathway database we demonstrate that when compared with a state-of-the-art alternative the CAMPways algorithm provides better alignment results on metabolic networks as far as measures based on same-pathway inclusion and biochemical significance are concerned. The execution speed of our algorithm constitutes yet another important improvement over alternative algorithms.
Citation Count: 34
A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera
(IEEE, 2014) Ar, İlktan; Akgül, Yusuf Sinan
Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However most methods in the literature view this task as a special case of motion recognition. In contrast we propose to employ the three main components of a physiotherapy exercise (the motion patterns the stance knowledge and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level which takes the advantage of domain knowledge for a more robust system. Finally a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red green and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation bodypart tracking joint detection and temporal segmentation methods. In the end favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.
Citation Count: 9
Early Steps in Automated Behavior Mapping via Indoor Sensors
(MDPI, 2017) Arsan, Taner; Kepez, Orçun
Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies we chose Wi-Fi BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m(2) test area (6m x 6 m) 0.86 m for the BLE beacon in a 37.44 m(2) test area (9.6 m x 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m(2) test area (6 m x 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m(2) test area (7.35 m x 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally we demonstrated the use of ultra-wide band sensor technology for BM.
Citation Count: 8
Growth kinetics of MPS-capped CdS quantum dots in self-assembled thin films
(Springer, 2012) Koç, Kenan; Tepehan, Fatma Zehra; Tepehan, Galip Gültekin
For this study we prepared colloidal CdS quantum dots using 3-mercaptopropyltrimethoxysilane as capping agent. Colloidal CdS quantum dots were directly deposited on glass substrates by a spin-coating process. Coated substrates were heat-treated between 225A degrees C and 325A degrees C for various heat treatment time intervals to investigate the growth kinetics of the quantum dots. Results showed that sizes of the CdS quantum dots grew approximately from 2.9 to 4.6 nm and the E (1s1s) energy values shifted approximately from 3.3 to 2.7 eV. Results showed that the average size of quantum dots increase by thermal treatment due to Ostwald ripening. The thermal process used to grow the size of quantum dots was examined according to the Lifshitz-Slyozov-Wagner theory. The activation energy of CdS quantum dots in thin films was calculated at approximately 44 kJ/mol.
Citation Count: 4
Improving performances of suboptimal greedy iterative biclustering heuristics via localization
(Oxford University Press, 2010) Erten, Cesim; Sözdinler, Melih
Motivation: Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. Even the simplest versions of the problem are computationally hard. Most of the proposed solutions therefore employ greedy iterative heuristics that locally optimize a suitably assigned scoring function. Methods: We provide a fast and simple pre-processing algorithm called localization that reorders the rows and columns of the input data matrix in such a way as to group correlated entries in small local neighborhoods within the matrix. The proposed localization algorithm takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. In order to evaluate the effectivenesss of the localization pre-processing algorithm we focus on three representative greedy iterative heuristic methods. We show how the localization pre-processing can be incorporated into each representative algorithm to improve biclustering performance. Furthermore we propose a simple biclustering algorithm Random Extraction After Localization (REAL) that randomly extracts submatrices from the localization pre-processed data matrix eliminates those with low similarity scores and provides the rest as correlated structures representing biclusters. Results: We compare the proposed localization pre-processing with another pre-processing alternative non-negative matrix factorization. We show that our fast and simple localization procedure provides similar or even better results than the computationally heavy matrix factorization pre-processing with regards to H-value tests. We next demonstrate that the performances of the three representative greedy iterative heuristic methods improve with localization pre-processing when biological correlations in the form of functional enrichment and PPI verification constitute the main performance criteria. The fact that the random extraction method based on localization REAL performs better than the representative greedy heuristic methods under same criteria also confirms the effectiveness of the suggested pre-processing method.
Citation Count: 3
In silico identification of critical proteins associated with learning process and immune system for Down syndrome
(Public Library Science, 2019) Dağ, Tamer; 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: 0
Machine Learning Approaches for Predicting Protein Complex Similarity
(Mary Ann Liebert Inc Publ, 2017) Farhoodi, Roshanak; Akbal-Delibas, Bahar; Haspel, Nurit
Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals electrostatic and desolvation forces) and the similarity of a conformation to its native structure the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network a multilayer neural network and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small often less than 1.5 angstrom error margin when trained with structures that have RMSD values of up to 7 angstrom. In our most recent experiments with the protein samples having RMSD values up to 27 angstrom the average prediction error was still relatively small attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.
Citation Count: 6
Novel Application Software for the Semi-Automated Analysis of Infrared Meibography Images
(2019) Dağ, Tamer; Gorcuyeva, Sona; Dağ, Tamer; Bozkurt, Banu
Purpose: To develop semi-automated application software that quickly analyzes infrared meibography images taken with the CSO Sirius Topographer (CSO, Italy) and to compare them to the manual analysis system on the device (Phoenix software platform). Methods: A total of 52 meibography images verified as high quality were used and analyzed through manual and semi-automated meibomian gland (MG) detector software in this study. For the manual method, an experienced researcher circumscribed the MGs by putting dots around grape-like clusters in a predetermined rectangular area, and Phoenix software measured the MG loss area by percentage, which took around 10 to 15 minutes. MG loss was graded from 1 (<25%) to 4 (severe >75%). For the semi-automated method, 2 blind physicians (I and II) determined the area to be masked by putting 5 to 6 dots on the raw images and measured the MG loss area using the newly developed semi-automated MG detector application software in less than 1 minute. Semi-automated measurements were repeated 3 times on different days, and the results were evaluated using paired-sample t test, Bland-Altman, and kappa κ analysis. Results: The mean MG loss area was 37.24% with the manual analysis and 40.09%, 37.89%, and 40.08% in the first, second, and third runs with the semi-automated analysis (P < 0.05). Manual analysis scores showed a remarkable correlation with the semi-automated analysis performed by 2 operators (r = 0.950 and r = 0.959, respectively) (P < 0.001). According to Bland-Altman analysis, the 95% limits of agreement between manual analysis and semi-automated analysis by operator I were between -10.69% and 5% [concordance correlation coefficient (CCC) = 0.912] and between -9.97% and 4.3% (CCC = 0.923) for operator II. The limit of interoperator agreement in semi-automated analysis was between -4.89% and 4.92% (CCC = 0.973). There was good to very good agreement in grading between manual and semi-automated analysis results (κ 0.76-0.84) and very good interoperator agreement with semi-automated software (κ 0.91) (P < 0.001). Conclusions: For the manual analysis of meibography images, around one hundred dots have to be put around grape-like clusters to determine the MGs, which makes the process too long and prone to errors. The newly developed semi-automated software is a highly reproducible, practical, and faster method to analyze infrared meibography images with excellent correlation with the manual analysis.
Citation Count: 0
Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems
(Hindawi Ltd, 2016) Bozkuş, Zeki; Bozkuş, Zeki
Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.
Citation Count: 7
RedNemo: topology-based PPI network reconstruction via repeated diffusion with neighborhood modifications
(Oxford University Press, 2017) Erten, Cesim; Erten, Cesim
Motivation: Analysis of protein-protein interaction (PPI) networks provides invaluable insight into several systems biology problems. High-throughput experimental techniques together with computational methods provide large-scale PPI networks. However a major issue with these networks is their erroneous nature
Citation Count: 1
Reliability-Oriented bioinformatic networks visualization
(Oxford University Press, 2011) Erten, Cesim; Erten, Cesim; Sözdinler, Melih
We present our protein-protein interaction (PPI) network visualization system RobinViz (reliability-oriented bioinformatic networks visualization). Clustering the PPI network based on gene ontology (GO) annotations or biclustered gene expression data providing a clustered visualization model based on a central/peripheral duality computing layouts with algorithms specialized for interaction reliabilities represented as weights completely automated data acquisition processing are notable features of the system.
Citation Count: 9
SiPAN: simultaneous prediction and alignment of protein-protein interaction networks
(Oxford University Press, 2015) Erten, Cesim; Erten, Cesim
Motivation: Network prediction as applied to protein-protein interaction (PPI) networks has received considerable attention within the last decade. Because of the limitations of experimental techniques for interaction detection and network construction several computational methods for PPI network reconstruction and growth have been suggested. Such methods usually limit the scope of study to a single network employing data based on genomic context structure domain sequence information or existing network topology. Incorporating multiple species network data for network reconstruction and growth entails the design of novel models encompassing both network reconstruction and network alignment since the goal of network alignment is to provide functionally orthologous proteins from multiple networks and such orthology information can be used in guiding interolog transfers. However such an approach raises the classical chicken or egg problem
Citation Count: 93
SPINAL: scalable protein interaction network alignment
(Oxford University Press, 2013) Erten, Cesim; Erten, Cesim
Motivation: Given protein-protein interaction (PPI) networks of a pair of species a pairwise global alignment corresponds to a one-to-one mapping between their proteins. Based on the presupposition that such a mapping provides pairs of functionally orthologous proteins accurately the results of the alignment may then be used in comparative systems biology problems such as function prediction/verification or construction of evolutionary relationships. Results: We show that the problem is NP-hard even for the case where the pair of networks are simply paths. We next provide a polynomial time heuristic algorithm SPINAL which consists of two main phases. In the first coarse-grained alignment phase we construct all pairwise initial similarity scores based on pairwise local neighborhood matchings. Using the produced similarity scores the fine-grained alignment phase produces the final one-to-one mapping by iteratively growing a locally improved solution subset. Both phases make use of the construction of neighborhood bipartite graphs and the contributors as a common primitive. We assess the performance of our algorithm on the PPI networks of yeast fly human and worm. We show that based on the accuracy measures used in relevant work our method outperforms the state-of-the-art algorithms. Furthermore our algorithm does not suffer from scalability issues as such accurate results are achieved in reasonable running times as compared with the benchmark algorithms.
Citation Count: 18
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

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