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Öğe A survey on computational methods used for drug repositioning(2025) Abushaaban, Eslam; Alhajj, RedaDrug repositioning offers a promising route to increase the efficacy and streamline the development of pharmaceutical treatments. With the high costs and extensive time frames associated with traditional drug development pathways, computational approaches have risen as pivotal alternatives. This paper surveys the latest network-based methodologies in computational drug repositioning, focusing on network analysis, machine learning, and deep learning methods. By examining the predictive accuracies of various methods across consistent datasets, we present a comparative analysis that reveals the strengths and potential of each category. Our findings highlight the evolution of computational strategies, emphasized by the growing complexity and predictive capabilities of recent models, especially those leveraging deep learning frameworksÖğe Diagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods(2025) Taneera, Sahar; Alhajj, RedaAutism Spectrum Disorder (ASD) is a developmental disorder that affects one’s interpersonal skills, communication, and the desire to engage in repetitive activities. Early detection is important for treatment and management to be beneficial. This implies that the search for strategies for diagnosing ASD as fast and as successfully as possible is quite urgent which leads us to ask What is the fastest and most accurate way to diagnose ASD at an early age? An electronic search of various databases was done up to the end of December 2023. This consisted of the Quality Assessment Tool for Diagnostic Accuracy Studies—2 which was applied to assess the quality of the chosen studies. In this review, 45 papers were used. Even simple diagnostic procedures such as ADOS and ADI-R showed moderate reliability but were time-consuming and dependent on clinicians’ skills. Machine learning and deep learning techniques proved to have the potential to diagnose ASD with the help of many datasets, which can enhance the diagnostic precision and speed of the process. Conclusions: The application of AI techniques in identifying ASD has been stated as beneficial where there are few facilities for clinical examination. More investigations should be carried out to establish the real-life relevance of these approaches.Öğe Recent advances in crack detection technologies for structures: a survey of 2022-2023 literature(2024) Kaveh, Hessam; Alhajj, RedaIntroduction: Cracks, as structural defects or fractures in materials like concrete, asphalt, and metal, pose significant challenges to the stability and safety of various structures. Addressing crack detection is of paramount importance due to its implications for public safety, infrastructure integrity, maintenance costs, asset longevity, preventive maintenance, economic impact, and environmental considerations. Methods: In this survey paper, we present a comprehensive analysis of recent advancements and developments in crack detection technologies for structures, with a specific focus on articles published between 2022 and 2023. Our methodology involves an exhaustive search of the Scopus database using keywords related to crack detection and machine learning techniques. Among the 129 papers reviewed, 85 were closely aligned with our research focus. Results: We explore datasets that underpin crack detection research, categorizing them as public datasets, papers with their own datasets, and those using a hybrid approach. The prevalence and usage patterns of public datasets are presented, highlighting datasets like Crack500, Crack Forest Dataset (CFD), and Deep Crack. Furthermore, papers employing proprietary datasets and those combining public and proprietary sources are examined. The survey comprehensively investigates the algorithms and methods utilized, encompassing CNN, YOLO, UNet, ResNet, and others, elucidating their contributions to crack detection. Evaluation metrics such as accuracy, precision, recall, F1-score, and IoU are discussed in the context of assessing model performance. The results of the 85 papers are summarized, demonstrating advancements in crack detection accuracy, efficiency, and applicability. Discussion: Notably, we observe a trend towards using modern and novel algorithms, such as Vision Transformers (ViT), and a shift away from traditional methods. The conclusion encapsulates the current state of crack detection research, highlighting the integration of multiple algorithms, expert models, and innovative data collection techniques. As a future direction, the adoption of emerging algorithms like ViT is suggested. This survey paper serves as a valuable resource for researchers, practitioners, and engineers working in the field of crack detection, offering insights into the latest trends, methodologies, and challenges.Öğe A survey of artificial intelligence/machine learning-based trends for prostate cancer analysis(2024) Sailunaz, Kashfia; Beştepe, Deniz; Alhajj, Lama; Özyer, Tansel; Rokne, Jon; Alhajj, RedaDifferent types of cancer are more commonly encountered recently. This may be attributed to a variety of reasons, including heredity, changes in the living conditions (food, drinks, pollution, etc.), advancement in technology which allowed for better diagnosis of diseases, among others. Prostate one of the main types of cancers witnessed in males; it has indeed been identified as the second type cancer leading to death in males. Accordingly, it has received considerable attention from the research community where computer scientists and data analysts are closely collaborating with pathologists to develop automated techniques and tools capable of classifying and identifying cancerous cases with high accuracy. These efforts are described in the literature in a large number of research articles which makes it hard and time consuming for researchers to grasp the current state of the art. Instead, review articles form a valuable source for researchers who are interesting in coping with the developments in the field. Generally, the literature includes several survey papers on prostate cancer; each of them tackles some aspect of the domain up to the time when the survey was prepared. Hence the need for the survey described in this paper which highlights the scope of each of the previous survey papers encountered in the literature and adds upon the latest developments in the field as described in more recent papers published mainly in 2023 and 2024. The survey focuses on the main artificial intelligence and machine learning techniques for diagnosing prostate cancer based on various types of data, including MRI. The most recent techniques employed in analyzing prostate cancer data, the various types of data, the available datasets, the reported results, etc. are all covered. This will help researchers in their efforts to keep track of the recent developments in the field and to realize the challenges which need more attention along the path towards developing robust and effect decision support systems for pathologists to have higher self confidence in handling their patients.Öğe A survey of machine learning-based methods for COVID-19 medical image analysis(Springer Science and Business Media Deutschland GmbH, 2023) Sailunaz, Kashfia; Özyer, Tansel; Rokne, Jon; Alhajj, RedaThe ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [Figure not available: see fulltext.]Öğe Preface(Springer Science and Business Media Deutschland GmbH, 2020) Alhajj, Reda; Moshirpour, Mohammad; Far, Behrouz[Abstract Not Available]Öğe Advances in quantitative analysis of astrocytes using machine learning(Wolters Kluwer Medknow Publications, 2023) Labate, Demetrio; Kayasandık, Cihan BilgeAstrocytes, a subtype of glial cells, are starshaped cells that are involved in the homeostasis and blood flow control of the central nervous system (CNS). They are known to provide structural and functional support to neurons, including the regulation of neuronal activation through extracellular ion concentrations, the regulation of energy dynamics in the brain through the transfer of lactate to neurons, and the modulation of synaptic transmission via the release of neurotransmitters such as glutamate and adenosine triphosphate. In addition, astrocytes play a critical role in neuronal reconstruction after brain injury, including neurogenesis, synaptogenesis, angiogenesis, repair of the blood-brain barrier, and glial scar formation after traumatic brain injury (Zhou et al., 2020). The multifunctional role of astrocytes in the CNS with tasks requiring close contact with their targets is reflected by their morphological complexity, with processes and ramifications occurring over multiple scales where interactions are plastic and can change depending on the physiological conditions. Another major feature of astrocytes is reactive astrogliosis, a process occurring in response to traumatic brain injury, neurological diseases, or infection which involves substantial morphological alterations and is often accompanied by molecular, cytoskeletal, and functional changes that ultimately play a key role in the disease outcome (Schiweck et al., 2018). Because morphological changes in astrocytes correlate so significantly with brain injury and the development of pathologies of the CNS, there is a major interest in methods to reliably detect and accurately quantify such morphological alterations. We review below the recent progress in the quantitative analysis of images of astrocytes. We remark that, while our discussion is focused on astrocytes, the same methods discussed below can be applied to other types of complex glial cells.Öğe A review of computational drug repositioning: Strategies, approaches, opportunities, challenges, and directions(BMC, 2020) Jarada, Tamer N.; Rokne, Jon George; Alhajj, RedaDrug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.Öğe Glaucoma detection using image processing techniques: A literature review(Elsevier, 2019) Sarhan, Abdullah; Rokne, Jon; Alhajj, RedaThe term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows: • approaches to segment different objects from both 2D and 3D images; • approaches that may lead to encouraging results for glaucoma detection; • challenges faced by researchers; and • currently available retinal datasets and evaluation methods.Öğe Computational approaches in antibody-drug conjugate optimization for targeted cancer therapy(Bentham Science Publishers Ltd, 2018) Melo, Rita; Lemos, Agostinho; Preto, Antonio Jose; Almeida, Jose Guilherme; Correia, Joao D. G.; Şensoy, Özge; Moreira, Irina SousaCancer has become one of the main leading causes of morbidity and mortality worldwide. One of the critical drawbacks of current cancer therapeutics has been the lack of the target-selectivity, as these drugs should have an effect exclusively on cancer cells while not perturbing healthy ones. In addition, their mechanism of action should be sufficiently fast to avoid the invasion of neighbouring healthy tissues by cancer cells. The use of conventional chemotherapeutic agents and other traditional therapies, such as surgery and radiotherapy, leads to off-target interactions with serious side effects. In this respect, recently developed target-selective Antibody-Drug Conjugates (ADCs) are more effective than traditional therapies, presumably due to their modular structures that combine many chemical properties simultaneously. In particular, ADCs are made up of three different units: a highly selective Monoclonal antibody (Mab) which is developed against a tumour-associated antigen, the payload (cytotoxic agent), and the linker. The latter should be stable in circulation while allowing the release of the cytotoxic agent in target cells. The modular nature of these drugs provides a platform to manipulate and improve selectivity and the toxicity of these molecules independently from each other. This in turn leads to generation of second-and third-generation ADCs, which have been more effective than the previous ones in terms of either selectivity or toxicity or both. Development of ADCs with improved efficacy requires knowledge at the atomic level regarding the structure and dynamics of the molecule. As such, we reviewed all the most recent computational methods used to attain all-atom description of the structure, energetics and dynamics of these systems. In particular, this includes homology modelling, molecular docking and refinement, atomistic and coarse-grained molecular dynamics simulations, principal component and cross-correlation analysis. The full characterization of the structure-activity relationship devoted to ADCs is critical for antibody-drug conjugate research and development.Öğe Modulation of protein-protein interactions for the development of effective therapeutics - from a joint perspective of experiment and computation(Bentham Science Publishers B.V., 2018) Moreira, Irina Sousa; Şensoy, ÖzgeMany proteins present in living organisms act as obligate oligomers – that is to say - they require other protein partners to function properly. Oligomerization does not only lead to the formation of physical interactions, which are required to hold individual protomers together in these assemblies, but it also triggers the cross-talk within the oligomer, so that individual protomers can regulate and modulate each other’s function in the form of either inhibition or activation. Consequently, this notion has changed the classical “single-target” pharmacology to “multi-target” one, urging the development of novel approaches in the field of drug discovery. In general, modulation of the function of a particular complex can be done by means of small-molecules developed specifically to target the interface between the protomers. This requires atomistic-level knowledge regarding the structure and dynamics of the system. As such, numerous experimental techniques were established in order to identify the partners in these assemblies. However, data solely based on these experimental techniques do not provide a mechanistic insight on the system as it cannot provide atomistic-level information per se regarding inherent allosteric interactions that govern the function of the complex. In this respect, computational methods act as indispensable tools to complement and to provide careful experimental data interpretation. The combination of these two worlds paves the way to the development of new, efficient and specific therapeutics.Öğe Utilization of biased G protein-coupled receptor signaling towards development of safer and personalized therapeutics(MDPI, 2019) İlter, Metehan; Mansoor, Samman; Şensoy, ÖzgeG protein-coupled receptors (GPCRs) are involved in a wide variety of physiological processes. Therefore, approximately 40% of currently prescribed drugs have targeted this receptor family. Discovery of beta-arrestin mediated signaling and also separability of G protein and beta-arrestin signaling pathways have switched the research focus in the GPCR field towards development of biased ligands, which provide engagement of the receptor with a certain effector, thus enriching a specific signaling pathway. In this review, we summarize possible factors that impact signaling profiles of GPCRs such as oligomerization, drug treatment, disease conditions, genetic background, etc. along with relevant molecules that can be used to modulate signaling properties of GPCRs such as allosteric or bitopic ligands, ions, aptamers and pepducins. Moreover, we also discuss the importance of inclusion of pharmacogenomics and molecular dynamics simulations to achieve a holistic understanding of the relation between genetic background and structure and function of GPCRs and GPCR-related proteins. Consequently, specific downstream signaling pathways can be enriched while those that bring unwanted side effects can be prevented on a patient-specific basis. This will improve studies that centered on development of safer and personalized therapeutics, thus alleviating the burden on economy and public health.Öğe Integrating personalized genomics into Turkish healthcare system: A cancer-oriented pilot activity of Istanbul Northern Anatolian Public Hospitals with GLAB(Kare Publishing, 2017) Doğanay, Levent; Özdil, Kamil; Memişoğlu, Kemal; Katrinli, Şeyma; Karakoç, Emre; Nikerel, Emrah; Dinler Doğanay, Gizem[Abstract Not Available]Öğe Prediction and targeting of interaction interfaces in g-rotein coupled receptor oligomers(Bentham Science Publishers Ltd, 2018) Schiedel, Anke C.; Köse, Meryem; Barreto, Carlos; Bueschbell, Beatriz; Morra, Giulia; Şensoy, Özge; Moreira, Irina SousaBackground: Communication within a protein complex is mediated by physical interactions made among the protomers. Evidence for both the allosteric regulation present among the protomers of the protein oligomer and of the direct effect of membrane composition on this regulation has made it essential to investigate the underlying molecular mechanism that drives oligomerization, the type of interactions present within the complex, and to determine the identity of the interaction interface. This knowledge allows a holistic understanding of dynamics and also modulation of the function of the resulting oligomers/signalling complexes. G-Protein-Coupled Receptors (GPCRs), which are targeted by 40% of currently prescribed drugs in the market, are widely involved in the formation of such physiological oligomers/signalling complexes. Scope: This review highlights the importance of studying Protein-Protein Interactions (PPI) by using a combination of data obtained from cutting-edge experimental and computational methods that were developed for this purpose. In particular, we focused on interaction interfaces found at GPCR oligomers as well as signalling complexes, since any problem associated with these interactions causes the onset of various crucial diseases. Conclusion: In order to have a holistic mechanistic understanding of allosteric PPIs that drive the formation of GPCR oligomers and also to determine the composition of interaction interfaces with respect to different membrane compositions, it is essential to combine both relevant experimental and computational data. In this way, efficient and specific targeting of these interaction interfaces in oligomers/complexes can be achieved. Thus, effective therapeutic molecules with fewer side effects can be designed to modulate the function of these physiologically important receptor family.
