WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Citation - WoS: 1
Citation - Scopus: 1
3-State Protein Secondary Structure Prediction Based on Scope Classes
(Inst Tecnologia Parana, 2021) Atasever, Sema; Azginoglu, Nuh; Erbay, Hasan; Aydin, Zafer
Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q(3) accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSI PRED on the SCOPe test datasets and found that our method outperformed PSI PRED.
Citation - WoS: 6
Citation - Scopus: 6
3-Sulfopropyl Methacrylate Based Cryogels as Potential Tissue Engineering Scaffolds
(Taylor & Francis Ltd, 2020) Durukan, Adile Yuruk; Isoglu, Ismail Alper
In this study, we developed cryogels containing 3-sulfopropyl methacrylate (SPMA) and 4-vinyl pyridine (4-VP) as a potential scaffold for tissue engineering applications. Cryogels with varying monomer ratios were synthesised by chemical cross-linking under cryogelation conditions. Effect of initiators and cross-linker amount (0.025-0.15 g MBA; 0.012-0.05 g APS; 2.5-12.5 mu l TEMED) and also freezing temperature (-20 and -80oC) were investigated, and the conditions were optimised according to the morphological structures examined by SEM. The functional groups of the materials were characterised by FT-IR. Compression test and swelling were applied to investigate mechanical properties and water absorption ability, respectively. As a preliminary study, selected materials were tested for cell cytotoxicity with MTT. According to our results, the ionic and biocompatible cryogels prepared in this study possessing a highly porous and interconnective structure with good mechanical characteristics and swelling properties can be suitable as tissue scaffolds for many applications.
3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
(Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
3Mont: A Multi-Omics Integrative Tool for Breast Cancer Subtype Stratification
(Public Library Science, 2025) Unlu Yazici, Miray; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, Malik
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.
Citation - WoS: 12
Citation - Scopus: 14
4D-QSAR Investigation and Pharmacophore Identification of Pyrrolo[2,1-C][1,4]Benzodiazepines Using Electron Conformational-Genetic Algorithm Method
(Taylor & Francis Ltd, 2016) Ozalp, A.; Yavuz, S. C.; Sabanci, N.; Copur, F.; Kokbudak, Z.; Saripinar, E.
In this paper, we present the results of pharmacophore identification and bioactivity prediction for pyrrolo[2,1-c][1,4]benzodiazepine derivatives using the electron conformational-genetic algorithm (EC-GA) method as 4D-QSAR analysis. Using the data obtained from quantum chemical calculations at PM3/HF level, the electron conformational matrices of congruity (ECMC) were constructed by EMRE software. The ECMC of the lowest energy conformer of the compound with the highest activity was chosen as the template and compared with the ECMCs of the lowest energy conformer of the other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA, i.e. pharmacophore) by ECSP software. A descriptor pool was generated taking into account the obtained pharmacophore. To predict the theoretical activity and select the best subset of variables affecting bioactivities, the nonlinear least square regression method and genetic algorithm were performed. For four types of activity including the GI(50), TGI, LC50 and IC50 of the pyrrolo[2,1-c][1,4] benzodiazepine series, the r(train)(2), r(test)(2) and q(2) values were 0.858, 0.810, 0.771; 0.853, 0.848, 0.787; 0.703, 0.787, 0.600; and 0.776, 0.722, 0.687, respectively.
Citation - WoS: 2
Citation - Scopus: 3
94.8 Km-Range Direct Detection Fiber Optic Distributed Acoustic Sensor
(Institute of Electrical and Electronics Engineers Inc., 2019) Uyar, Faruk; Onat, Talha; Unal, Canberk; Kartaloǧlu, Tolga; Ozdur, Ibrahim T.; Özbay, Ekmel
This work demonstrates an ultra-long range direct detection fiber optic distributed acoustic sensor which can detect vibrations at a distance of 94.8 km with 10 m resolution along the sensing fiber. © 2019 Elsevier B.V., All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
Ab Initio Simulation of Amorphous BC3
(Elsevier, 2020) Durandurdu, Murat
We report the structural and electrical properties of an amorphous BC3 model based on ab initio molecular dynamics simulations. The amorphous network is achieved from the melt and has a layer-like structure consisting of mainly hexagonal (six membered) rings as in the crystal. However, the distribution of boron atoms in the noncrystalline configuration appears to differ significantly from that of boron atoms in the crystal. The network is a solid solution and has randomly distributed nanosized graphene-like domains at each layer. Boron atoms have a tendency to form more overcoordinated defects involving with boron-boron homopolar bond(s). The mean coordination of boron and carbon atoms is 3.2 and 3.0, respectively. Interestingly the amorphous configuration is found to have a slightly higher density and bulk modulus than the crystal, which are attributed to the existence of overcoordinated units in the amorphous state. Based on the localization of the band tail states, noncrystalline BC3 is speculated to be a semiconducting material.
Citation - WoS: 3
Citation - Scopus: 2
Ab Initio Study of Boron-Rich Amorphous Boron Carbides
(Wiley, 2023) Yildiz, Tevhide Ayca; Durandurdu, Murat
Amorphous boron carbide compositions having high B contents (BxC1-x, 0.50 <= x <= 0.95) are systematically created by way of ab initio molecular dynamics calculations, and their structural, electrical, and mechanical characteristics are inclusively investigated. The coordination number of both B and C atoms increases progressively with increasing B/C ratio and more close-packed materials having pentagonal pyramid motifs form. An amorphous diamond-like local arrangement is found to be dominant up to 65% B content, and beyond this content, a mixed state of amorphous diamond- and B-like structures is perceived in the models because sp(3) hybridization around C atoms is still leading one for all compositions. The pentagonal pyramid motifs around C atoms are anticipated to appear beyond 65% content. The intericosahedral linear C-B-C chains do not form in any model. All amorphous boron carbides are semiconducting materials. The mechanical properties gradually increase with increasing B concentration, and some amorphous compositions are proposed to be hard materials on the basis of their Vickers hardness estimation.
Citation - WoS: 6
Citation - Scopus: 6
Absorption Enhancement by Semi-Cylindrical Structures for an Organic Solar Cell Application
(Optical Soc Amer, 2020) Hah, Dooyoung
Organic solar cells are attractive for various applications with their flexibility and low-cost manufacturability. In order to increase their attractiveness in practice, it is essential to improve their energy conversion efficiency. In this work, semi-cylindrical-shell-shaped structures are proposed as one of the approaches, aiming at absorption enhancement in an organic solar cell. Poly(3-hexylthiophene-2,5-diyl) blended with indene-C60 bisadduct (P3HT:ICBA) is considered as the active layer. Light coupling to the guided modes and a geometrical advantage are attributed to this absorption enhancement. Finite-difference time-domain methods and finite element analysis are used to examine the absorption spectra for two types of devices, i.e., a debossed type and an embossed type. It is shown that absorption enhancement increases as the radius of the cylinder increases, but reaches a saturation at about 4-mu m radius. The average absorption enhancement with an active layer thickness of 200 nm and radius of 4 mu m, and for incidence angles between 0 degrees and 70 degrees, is found as 51%-52% for TE-polarized input and as 30%-33% for TM-polarized input when compared to a flat structure. Another merit of the proposed structures is that the range of incidence angles where the integrated absorption is at the level of the normal incidence is significantly broadened, reaching 70 degrees-80 degrees. This feature can be highly useful especially when organic solar cells are to be placed around a round object. The study results also exhibit that the proposed devices bear broadband absorption characteristics. (C) 2020 Optical Society of America
Citation - WoS: 2
Citation - Scopus: 2
Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems
(Wiley, 2025) Hacilar, Hilal; Dedeturk, Bilge Kagan; Ozmen, Mihrimah; Celik, Mehlika Eraslan; Gungor, Vehbi Cagri
Metaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.
Citation - WoS: 1
Citation - Scopus: 1
Achieving High Optical Absorption in Thin Film Photovoltaic Devices via Nanopillar Arrays and Metal Nanoparticles
(Wiley-VCH Verlag GmbH, 2025) Tut, Turgut
In this study, crystalline silicon nanopillars has been employed as a hexagonal array photonic crystal structure with low optical reflection, augmented by silver metallic nanoparticles ranging from 10 to 50 nm in diameter in order to achieve high absorption in thin silicon films, a critical factor for applications in photovoltaic devices. Initially, it has been begun with an optimized structure in terms of pillar filling ratio, pillar height, and diameter, as established in the previous study. This allows to obtain a hexagonal array of nanopillars with a surface characterized by low optical reflection. To enhance the optical absorption within the bulk of the silicon thin film, the optical scattering properties of silver (Ag) metallic nanoparticles (MNPs) has been harnessed. The integration of silver metal nanoparticles into the photonic crystal hexagonal nanopillar array involved introducing a cavity into the silicon pillar. Placing Ag MNPs near the bottom of the cavity prevented the degradation of the photonic crystal's ability to maintain low reflection within the desired optical spectrum (between 400-1100 nm). Comparison between the nanopillar hexagonal array structure with Ag MNPs and the bare silicon substrate revealed a remarkable 104.76 percent increase in optical absorption for a 1-micron thick silicon bulk material. This triple hybrid structure exhibits tremendous potential in photovoltaic device applications, including solar cells and photodetectors, with the capacity to significantly enhance conversion efficiency.
Citation - WoS: 5
Citation - Scopus: 4
Active Subnetwork Ga: A Two Stage Genetic Algorithm Approach to Active Subnetwork Search
(Bentham Science Publ Ltd, 2017) Ozisik, Ozan; Bakir-Gungor, Burcu; Diri, Banu; Sezerman, Osman Ugur
Background: A group of interconnected genes in a protein-protein interaction network that contains most of the disease associated genes is called an active subnetwork. Active subnetwork search is an NP-hard problem. In the last decade, simulated annealing, greedy search, color coding, genetic algorithm, and mathematical programming based methods are proposed for this problem. Method: In this study, we employed a novel genetic algorithm method for active subnetwork search problem. We used active node list chromosome representation, branch swapping crossover operator, multicombination of branches in crossover, mutation on duplicate individuals, pruning, and two stage genetic algorithm approach. The proposed method is tested on simulated datasets and Wellcome Trust Case Control Consortium rheumatoid arthritis genome-wide association study dataset. Our results are compared with the results of a simple genetic algorithm implementation and the results of the simulated annealing method that is proposed by Ideker et al. in their seminal paper. Results and Conclusion: The comparative study demonstrates that our genetic algorithm approach outperforms the simple genetic algorithm implementation in all datasets and simulated annealing in all but one datasets in terms of obtained scores, although our method is slower. Functional enrichment results show that the presented approach can successfully extract high scoring subnetworks in simulated datasets and identify significant rheumatoid arthritis associated subnetworks in the real dataset. This method can be easily used on the datasets of other complex diseases to detect disease-related active subnetworks. Our implementation is freely available at https://www.ce.yildiz.edu.tr/personal/ozanoz/file/6611/ActSubGA.
Citation - WoS: 15
Citation - Scopus: 20
Adaptive Fault Detection Scheme Using an Optimized Self-Healing Ensemble Machine Learning Algorithm
(China Electric Power Research inst, 2022) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Li, Xiangjun; Muyeen, S. M.
This paper proposes a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose, power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.
Adaptive Re-Use of Medieval Caravanserais in Central Anatolia
(Gangemi Editore S P A, 2019) Yoney, Nilufer Baturayolu; Asiliskender, Burak; Urfalioglu, Nur
Kayseri, located at the junction of two major trade routes from northeast to southwest and from southeast to northwest, has been a commercial center for at least 4,000 years. The 23,500 tablets found at the Assyrian trade colony in Kanesh-Karum dating around 2,000 BCE and located 20km from the modern city provide ample proof. The great number and relevant size of Medieval caravanserais around the city as well as commercial buildings at the center indicate that this importance continued. Some of these caravanserais are already in use, albeit with inadequate architectural preservation measures while others are abandoned and/or partially destroyed. Indeed, the preservation, restoration and adaptive re-use of Medieval buildings is a major problematic, bringing out issues and interventions related to lacunae and reintegration, liberation or clearance of additions, structural strengthening with traditional/contemporary technologies, partial reconstruction, consolidation, cleaning and conservation of original building materials, and preventive maintenance. This paper aims to consider the possible presentation and adaptive re-use of Seljukid caravanserais over and inventory of accessible and at least partially preserved examples, focusing on eight case studies from the late 12th and 13th centuries: Karatay Han (1240), Tuzhisar Sultan Han (1232-1236), Eshab-i Kehf Han (before 1235), Cirgalan Han, Saruhan, Agzikarahan (1231-1240), Alayhan and Oresin Han.
Citation - WoS: 1
Citation - Scopus: 1
Addressing the Modern Regimes of Urban Spectacle: Revisiting the Ottoman General Exhibition of 1863 in Istanbul
(Sage Publications inc, 2024) Tozoglu, Ahmet Erdem
One of the most spectacular events of the Ottoman experience of modernity was the inauguration of the Ottoman General Exposition in Istanbul in 1863. The ancient Hippodrome, which is one of the most prominent venues of the city and the setting of memorable celebrations and festivals for centuries, hosted the event and provided the visitors with the opportunity to become part of the modern regimes of gaze and spectacle. This article posits three observer roles to reveal the multilayered structure of urban spectacle in mid-century Istanbul, namely the sultanic gaze, spectacle of the ordinary citizens, and the mediated experience of the foreigner. To understand the particularities of each position, I utilize several visual and textual documents about the exhibition event. Though just a single case in Ottoman urban history, the exposition enables us to understand how the new manner of modern urban spectacle emerged during a spectacular public event in Istanbul.
Citation - WoS: 6
Citation - Scopus: 5
Admissible Invariants of Genus 3 Curves
(Springer Heidelberg, 2015) Cinkir, Zubeyir
Several invariants of polarized metrized graphs and their applications in Arithmetic Geometry are studied recently. In this paper, we explicitly calculated these admissible invariants for all curves of genus 3. We find the sharp lower bound for the invariants phi, lambda and epsilon for all polarized metrized graphs of genus 3. This improves the lower bound given for Effective Bogomolov Conjecture for such curves.
Adult Zebrafish Brain as a Demyelination Model and Role of WNF Signaling in Remyelination
(Wiley, 2024) Bora, U.; Demirbasoglu, E. S.; Turhanlar-Sahin, E.; Guner, H.; Ozhan, G.
Citation - WoS: 4
Citation - Scopus: 4
Advanced Hybrid Machine Learning Methods for Predicting Rainfall Time Series: The Situation at the Kütahya Station in Türkiye
(Springer Heidelberg, 2025) Ilkentapar, Mucella; Citakoglu, Hatice; Talebi, Hamed; Akturk, Gaye; Spor, Pinar; Caglar, Yasin; Aksit, Serhat
Long-term variations in rainfall patterns, known as rainfall variability, have increasingly impacted ecological and socioeconomic systems, particularly in regions with high sensitivity. Consequently, accurate forecasting of rainfall at both short- and long-term time scales is essential, necessitating a comprehensive analysis of historical rainfall time series data collected from meteorological stations. In this study, K & uuml;tahya Province was selected as the study area, utilizing monthly rainfall data from its sole meteorological station spanning the period from 1960 to 2023. The dataset was partitioned into a training set (January 1960-March 2008) and a test set (April 2008-December 2023). Lagged rainfall values at t-1, t-2, and t-3 were used as input variables to predict rainfall at time t. The primary objective of this research is to assess the effectiveness of various preprocessing techniques in developing hybrid machine learning models for rainfall prediction. Gaussian Process Regression (GPR), Support Vector Machines, and Adaptive Neuro-Fuzzy Inference System were employed as machine learning methods. Furthermore, multiple signal decomposition techniques, including Complete Ensemble Empirical Mode Decomposition (CEEMD), Tunable Q-Factor Wavelet Transform, Empirical Mode Decomposition, Robust Empirical Mode Decomposition, Variational Mode Decomposition, Empirical Wavelet Transform, and Ensemble Empirical Mode Decomposition (EEMD), were utilized as preprocessing steps to enhance model performance. The predictive performance of the developed hybrid models was evaluated using various statistical measures. Among the evaluated models, the CEEMD-GPR hybrid model exhibited the best prediction performance with Coefficient of Determination (R2 = 0.998) and Nash-Sutcliffe Efficiency (NSE = 0.998) values close to 1, Mean Absolute Error (MAE = 1.42) and Mean Squared Error (RMSE = 1.79) values close to zero. These findings indicate that CEEMD demonstrated superior decomposition efficiency compared to the other six decomposition techniques. Additionally, the Kruskal-Wallis test conducted during the analysis phase yielded a statistical significance level of p > 0.05, confirming that the observed and predicted rainfall data originated from the same distribution. Consequently, the effectiveness and reliability of the proposed hybrid models for rainfall prediction were validated.
Citation - WoS: 12
Citation - Scopus: 14
Advanced Tunability of Optical Properties of CdS/ZnSe Multi-Shell Quantum Dot by the Band Edge Engineering
(Elsevier, 2023) Koc, Fatih; Kavruk, Ahmet Emre; Sahin, Mehmet
In this study, the advanced manipulability of wave functions in a type-II multi-shell hetero-nanostructure (MS-HNS) and the tunability of radiative exciton lifetime over a wide range with and/or without changing in transition energies has been demonstrated by the band edge engineering. For this purpose, the electronic and optical properties of exciton (X) and biexciton (XX) in a spherical CdS/ZnSe/ZnTe/CdSe HNS have been explored in detail. In the calculations, effects of all Coulombic interactions between the charges have been taken into account on the wave functions. Moreover, in the case of XX, the exchange-correlation potential between the same charged particles has also been considered. The results have been presented as a function of CdS core radius and ZnSe shell thickness and the probable physical reasons have been discussed in detail.
Citation - WoS: 35
Citation - Scopus: 39
Advances in Micelle-Based Drug Delivery: Cross-Linked Systems
(Bentham Science Publ Ltd, 2017) Isoglu, Ismail Alper; Ozsoy, Yildiz; Isoglu, Sevil Dincer
There are several barriers that drug molecules encounter in body beginning from kidney filtration and reticulo-endothelial system (RES) clearance to cellular trafficking. Multifunctional nanocarriers have a great potential for the delivery of drugs by enhancing therapeutic activity of existing methodologies. A variety of nanocarriers are constructed by different material types, which have unique physicochemical properties for drug delivery applications. Micelles formed by amphiphilic polymers are one of the most important drug/nanocarrier formulation products, in which the core part is suitable for encapsulation of hydrophobic agent whereas the outer shell can be utilized for targeting the drug to the disease area. Micelles as self-assembled nanostructures may encounter difficulties in biodistribution of encapsulated drugs because they have a tendency to be dissociated in dilution or high ionic strength. Therefore, therapeutic efficiency is decreased and it requires high amount of drug to be administered to achieve more efficient result. To overcome this problem, covalently stabilized structures produced by cross-linking in core or shell part, which can prevent the micelle dissociation and regulate drug release, have been proposed. These systems can be designed as responsive systems in which cross-links are degradable or hydrolysable under specific conditions such as low pH or reductive environment. These are enhancing characteristics in drug delivery because their cleavage allows the release of bioactive agent encapsulated in the carrier at a certain site or time. This review describes the chemical methodologies for the preparation of cross-linked micelles, and reports an update of latest studies in literature.

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