Identification Of Streptococcus Pyogenes Using Raman Spectroscopy

Despite the attention that Raman Spectroscopy has gained recently in the area of pathogen identification, the spectra analyses techniques are not well developed. In most scenarios, they rely on expert intervention to detect and assign the peaks of the spectra to specific molecular vibration. Although some investigators have used machine-learning techniques to classify pathogens, these studies are usually limited to a specific application, and the generalization of these techniques is not clear. Also, a wide range of algorithms have been developed for classification problems, however, there is less insight to applying such methods on Raman spectra. Furthermore, analyzing the Raman spectra requires pre-processing of the raw spectra, in particular, background removing. Various techniques are developed to remove the background of the raw spectra accurately and with or without less expert intervention. Nevertheless, as the background of the spectra varies in the different media, these methods still require expert effort adding complexity and inefficiency to the identification task. This dissertation describes the development of state-of-the-art classification techniques to identify S. pyogenes from other species, including water and other confounding background pathogens. We compared these techniques in terms of their classification accuracy, sensitivity, and specificity in addition to providing a bias-variance insight in selecting the number of principal components in a principal component analysis (PCA). It was observed that Random Forest provided a better result with an accuracy of 94.11%. Next, a novel deep learning technique was developed to remove the background of the Raman spectra and then identify the pathogen. The architecture of the network was discussed and it was found that this method yields an accuracy of 100% in our test samples. This outperforms other traditional machine learning techniques as discussed. In clinical applications of Raman Spectroscopy, the samples have confounding background creates a challenging task for the removal of the spectral background and subsequent identification of the pathogen in real- time. We tested our methodology on datasets composed of confounding background such as throat swabs from patients and discussed the robustness and generalization of the developed method. It was found that the misclassification error of the test dataset was around 3.7%. Also, the realization of the trained model is discussed in detail to provide a better understating and insight into the efficacy of the deep learning architecture. This technique provides a platform for general analysis of other pathogens in confounding environments as well

Machine learning for text classification in building management systems

Copyright © 2022 The Author(s). In building management systems (BMS), a medium building may have between 200 and 1000 sensor points. Their labels need to be translated into a naming standard so they can be automatically recognised by the BMS platform. The current industrial practices often manually translate these points into labels (this is known as the tagging process), which takes around 8 hours for every 100 points. We introduce an AI-based multi-stage text classification that translates BMS points into formatted BMS labels. After comparing five different techniques for text classification (logistic regression, random forests, XGBoost, multinomial Naive Bayes and linear support vector classification), we demonstrate that XGBoost is the top performer with 90.29% of true positives, and use the prediction confidence to filter out false positives. This approach can be applied in sensors networks in various applications, where manual free-text data pre-processing remains cumbersome.Department for Business, Energy and Industrial Strategy of the United Kingdom; Brunel University Londo

Study of Morphology Based Cell Assay by Diffraction Imaging Flow Cytometry

Development of an accurate and label-free method for single cell assay attracts intensive research efforts for its importance to cell biology research and clinicG14G7al applications. Flow cytometry is one of the most widely used technologies for rapid assay of single cells but existing approaches provide very limited information on cell morphology and require the fluorescence staining. In this dissertation research, we focus our efforts on the quantitative analysis of cell morphology using confocal microscopy based three-dimensional (3D) reconstruction and the exploration of a new approach of flow cytometry through imaging of highly coherent scattered light. The goal of the dissertation research is to develop a new and morphology-based approach for rapid cell assay and phenotyping with the polarization diffraction imaging flow cytometry (p-DIFC) platform through investigation of the structure-function relations at the cell level. To achieve this goal, cross-polarized diffraction image pairs have been acquired from single cells excited by a linearly polarized laser beam. Image texture and intensity parameters are extracted with a gray level co-occurrence matrix (GLCM) algorithm to obtain a set of image parameters to quantify the diffraction patterns. An automated cell classification method has been developed using a Support Vector Machine (SVM) algorithm in the feature space formed by the training data of the cross-polarized diffraction image pairs. We have investigated different types of human lymphocytes and prostate epithelial cells with the confocal imaging and p-DIFC measurements and conducted cell morphology and classification studies. The analysis of 3D morphology parameters among the six types of cells provides, for the first time, the ability to quantitatively evaluate the morphologic differences among these phenotypes and to gain insights on the morphology-based classification. It has been further shown that the diffraction image parameters can be mapped into a high-dimensional feature space with the SVM algorithm to obtain the optimized model and yield accurate classifications between Jurkat T cells and Ramos B cells and between the normal and cancerous prostate epithelial cells. Based on these results we conclude that the p-DIFC method has significant potentials to be developed into a rapid and label-free method for cell assay and morphology-based classification to discriminate cells of high similarity in their morphology.Ph.D

Learning for Optimization with Virtual Savant

Optimization problems arising in multiple fields of study demand efficient algorithms that can exploit modern parallel computing platforms. The remarkable development of machine learning offers an opportunity to incorporate learning into optimization algorithms to efficiently solve large and complex problems. This thesis explores Virtual Savant, a paradigm that combines machine learning and parallel computing to solve optimization problems. Virtual Savant is inspired in the Savant Syndrome, a mental condition where patients excel at a specific ability far above the average. In analogy to the Savant Syndrome, Virtual Savant extracts patterns from previously-solved instances to learn how to solve a given optimization problem in a massively-parallel fashion. In this thesis, Virtual Savant is applied to three optimization problems related to software engineering, task scheduling, and public transportation. The efficacy of Virtual Savant is evaluated in different computing platforms and the experimental results are compared against exact and approximate solutions for both synthetic and realistic instances of the studied problems. Results show that Virtual Savant can find accurate solutions, effectively scale in the problem dimension, and take advantage of the availability of multiple computing resources.Los problemas de optimización que surgen en múltiples campos de estudio demandan algoritmos eficientes que puedan explotar las plataformas modernas de computación paralela. El notable desarrollo del aprendizaje automático ofrece la oportunidad de incorporar el aprendizaje en algoritmos de optimización para resolver problemas complejos y de grandes dimensiones de manera eficiente. Esta tesis explora Savant Virtual, un paradigma que combina aprendizaje automático y computación paralela para resolver problemas de optimización. Savant Virtual está inspirado en el Sı́ndrome de Savant, una condición mental en la que los pacientes se destacan en una habilidad especı́fica muy por encima del promedio. En analogı́a con el sı́ndrome de Savant, Savant Virtual extrae patrones de instancias previamente resueltas para aprender a resolver un determinado problema de optimización de forma masivamente paralela. En esta tesis, Savant Virtual se aplica a tres problemas de optimización relacionados con la ingenierı́a de software, la planificación de tareas y el transporte público. La eficacia de Savant Virtual se evalúa en diferentes plataformas informáticas y los resultados se comparan con soluciones exactas y aproximadas para instancias tanto sintéticas como realistas de los problemas estudiados. Los resultados muestran que Savant Virtual puede encontrar soluciones precisas, escalar eficazmente en la dimensión del problema y aprovechar la disponibilidad de múltiples recursos de cómputo.Fundación Carolina Agencia Nacional de Investigación e Innovación (ANII, Uruguay) Universidad de Cádiz Universidad de la Repúblic

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Challenges in biomedical data science: data-driven solutions to clinical questions

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific domains, the amount of data involved in each decision-making process has become overwhelming. The complexity of the phenomena under investigation and the scale of modern data collections has long superseded human analysis and insights potential

Efficient feature reduction and classification methods

Durch die steigende Anzahl verfügbarer Daten in unterschiedlichsten Anwendungsgebieten nimmt der Aufwand vieler Data-Mining Applikationen signifikant zu. Speziell hochdimensionierte Daten (Daten die über viele verschiedene Attribute beschrieben werden) können ein großes Problem für viele Data-Mining Anwendungen darstellen. Neben höheren Laufzeiten können dadurch sowohl für überwachte (supervised), als auch nicht überwachte (unsupervised) Klassifikationsalgorithmen weitere Komplikationen entstehen (z.B. ungenaue Klassifikationsgenauigkeit, schlechte Clustering-Eigenschaften, …). Dies führt zu einem Bedarf an effektiven und effizienten Methoden zur Dimensionsreduzierung. Feature Selection (die Auswahl eines Subsets von Originalattributen) und Dimensionality Reduction (Transformation von Originalattribute in (Linear)-Kombinationen der Originalattribute) sind zwei wichtige Methoden um die Dimension von Daten zu reduzieren. Obwohl sich in den letzten Jahren vielen Studien mit diesen Methoden beschäftigt haben, gibt es immer noch viele offene Fragestellungen in diesem Forschungsgebiet. Darüber hinaus ergeben sich in vielen Anwendungsbereichen durch die immer weiter steigende Anzahl an verfügbaren und verwendeten Attributen und Features laufend neue Probleme. Das Ziel dieser Dissertation ist es, verschiedene Fragenstellungen in diesem Bereich genau zu analysieren und Verbesserungsmöglichkeiten zu entwickeln. Grundsätzlich, werden folgende Ansprüche an Methoden zur Feature Selection und Dimensionality Reduction gestellt: Die Methoden sollten effizient (bezüglich ihres Rechenaufwandes) sein und die resultierenden Feature-Sets sollten die Originaldaten möglichst kompakt repräsentieren können. Darüber hinaus ist es in vielen Anwendungsgebieten wichtig, die Interpretierbarkeit der Originaldaten beizubehalten. Letztendlich sollte der Prozess der Dimensionsreduzierung keinen negativen Effekt auf die Klassifikationsgenauigkeit haben - sondern idealerweise, diese noch verbessern. Offene Problemstellungen in diesem Bereich betreffen unter anderem den Zusammenhang zwischen Methoden zur Dimensionsreduzierung und der resultierenden Klassifikationsgenauigkeit, wobei sowohl eine möglichst kompakte Repräsentation der Daten, als auch eine hohe Klassifikationsgenauigkeit erzielt werden sollen. Wie bereits erwähnt, ergibt sich durch die große Anzahl an Daten auch ein erhöhter Rechenaufwand, weshalb schnelle und effektive Methoden zur Dimensionsreduzierung entwickelt werden müssen, bzw. existierende Methoden verbessert werden müssen. Darüber hinaus sollte natürlich auch der Rechenaufwand der verwendeten Klassifikationsmethoden möglichst gering sein. Des Weiteren ist die Interpretierbarkeit von Feature Sets zwar möglich, wenn Feature Selection Methoden für die Dimensionsreduzierung verwendet werden, im Fall von Dimensionality Reduction sind die resultierenden Feature Sets jedoch meist Linearkombinationen der Originalfeatures. Daher ist es schwierig zu überprüfen, wie viel Information einzelne Originalfeatures beitragen. Im Rahmen dieser Dissertation konnten wichtige Beiträge zu den oben genannten Problemstellungen präsentiert werden: Es wurden neue, effiziente Initialisierungsvarianten für die Dimensionality Reduction Methode Nonnegative Matrix Factorization (NMF) entwickelt, welche im Vergleich zu randomisierter Initialisierung und im Vergleich zu State-of-the-Art Initialisierungsmethoden zu einer schnelleren Reduktion des Approximationsfehlers führen. Diese Initialisierungsvarianten können darüber hinaus mit neu entwickelten und sehr effektiven Klassifikationsalgorithmen basierend auf NMF kombiniert werden. Um die Laufzeit von NMF weiter zu steigern wurden unterschiedliche Varianten von NMF Algorithmen auf Multi-Prozessor Systemen vorgestellt, welche sowohl Task- als auch Datenparallelismus unterstützen und zu einer erheblichen Reduktion der Laufzeit für NMF führen. Außerdem wurde eine effektive Verbesserung der Matlab Implementierung des ALS Algorithmus vorgestellt. Darüber hinaus wurde eine Technik aus dem Bereich des Information Retrieval -- Latent Semantic Indexing -- erfolgreich als Klassifikationsalgorithmus für Email Daten angewendet. Schließlich wurde eine ausführliche empirische Studie über den Zusammenhang verschiedener Feature Reduction Methoden (Feature Selection und Dimensionality Reduction) und der resultierenden Klassifikationsgenauigkeit unterschiedlicher Lernalgorithmen präsentiert. Der starke Einfluss unterschiedlicher Methoden zur Dimensionsreduzierung auf die resultierende Klassifikationsgenauigkeit unterstreicht dass noch weitere Untersuchungen notwendig sind um das komplexe Zusammenspiel von Dimensionsreduzierung und Klassifikation genau analysieren zu können.The sheer volume of data today and its expected growth over the next years are some of the key challenges in data mining and knowledge discovery applications. Besides the huge number of data samples that are collected and processed, the high dimensional nature of data arising in many applications causes the need to develop effective and efficient techniques that are able to deal with this massive amount of data. In addition to the significant increase in the demand of computational resources, those large datasets might also influence the quality of several data mining applications (especially if the number of features is very high compared to the number of samples). As the dimensionality of data increases, many types of data analysis and classification problems become significantly harder. This can lead to problems for both supervised and unsupervised learning. Dimensionality reduction and feature (subset) selection methods are two types of techniques for reducing the attribute space. While in feature selection a subset of the original attributes is extracted, dimensionality reduction in general produces linear combinations of the original attribute set. In both approaches, the goal is to select a low dimensional subset of the attribute space that covers most of the information of the original data. During the last years, feature selection and dimensionality reduction techniques have become a real prerequisite for data mining applications. There are several open questions in this research field, and due to the often increasing number of candidate features for various application areas (e.\,g., email filtering or drug classification/molecular modeling) new questions arise. In this thesis, we focus on some open research questions in this context, such as the relationship between feature reduction techniques and the resulting classification accuracy and the relationship between the variability captured in the linear combinations of dimensionality reduction techniques (e.\,g., PCA, SVD) and the accuracy of machine learning algorithms operating on them. Another important goal is to better understand new techniques for dimensionality reduction, such as nonnegative matrix factorization (NMF), which can be applied for finding parts-based, linear representations of nonnegative data. This ``sum-of-parts'' representation is especially useful if the interpretability of the original data should be retained. Moreover, performance aspects of feature reduction algorithms are investigated. As data grow, implementations of feature selection and dimensionality reduction techniques for high-performance parallel and distributed computing environments become more and more important. In this thesis, we focus on two types of open research questions: methodological advances without any specific application context, and application-driven advances for a specific application context. Summarizing, new methodological contributions are the following: The utilization of nonnegative matrix factorization in the context of classification methods is investigated. In particular, it is of interest how the improved interpretability of NMF factors due to the non-negativity constraints (which is of central importance in various problem settings) can be exploited. Motivated by this problem context two new fast initialization techniques for NMF based on feature selection are introduced. It is shown how approximation accuracy can be increased and/or how computational effort can be reduced compared to standard randomized seeding of the NMF and to state-of-the-art initialization strategies suggested earlier. For example, for a given number of iterations and a required approximation error a speedup of 3.6 compared to standard initialization, and a speedup of 3.4 compared to state-of-the-art initialization strategies could be achieved. Beyond that, novel classification methods based on the NMF are proposed and investigated. We can show that they are not only competitive in terms of classification accuracy with state-of-the-art classifiers, but also provide important advantages in terms of computational effort (especially for low-rank approximations). Moreover, parallelization and distributed execution of NMF is investigated. Several algorithmic variants for efficiently computing NMF on multi-core systems are studied and compared to each other. In particular, several approaches for exploiting task and/or data-parallelism in NMF are studied. We show that for some scenarios new algorithmic variants clearly outperform existing implementations. Last, but not least, a computationally very efficient adaptation of the implementation of the ALS algorithm in Matlab 2009a is investigated. This variant reduces the runtime significantly (in some settings by a factor of 8) and also provides several possibilities to be executed concurrently. In addition to purely methodological questions, we also address questions arising in the adaptation of feature selection and classification methods to two specific application problems: email classification and in silico screening for drug discovery. Different research challenges arise in the contexts of these different application areas, such as the dynamic nature of data for email classification problems, or the imbalance in the number of available samples of different classes for drug discovery problems. Application-driven advances of this thesis comprise the adaptation and application of latent semantic indexing (LSI) to the task of email filtering. Experimental results show that LSI achieves significantly better classification results than the widespread de-facto standard method for this special application context. In the context of drug discovery problems, several groups of well discriminating descriptors could be identified by utilizing the ``sum-of-parts`` representation of NMF. The number of important descriptors could be further increased when applying sparseness constraints on the NMF factors

Distributed analysis of vertically partitioned sensor measurements under communication constraints

Nowadays, large amounts of data are automatically generated by devices and sensors. They measure, for instance, parameters of production processes, environmental conditions of transported goods, energy consumption of smart homes, traffic volume, air pollution and water consumption, or pulse and blood pressure of individuals. The collection and transmission of data is enabled by electronics, software, sensors and network connectivity embedded into physical objects. The objects and infrastructure connecting such objects are called the Internet of Things (IoT). In 2010, already 12.5 billion devices were connected to the IoT, a number about twice as large as the world's population at that time. The IoT provides us with data about our physical environment, at a level of detail never known before in human history. Understanding such data creates opportunities to improve our way of living, learning, working, and entertaining. For instance, the information obtained from data analysis modules embedded into existing processes could help their optimization, leading to more sustainable systems which save resources in sectors such as manufacturing, logistics, energy and utilities, the public sector, or healthcare. IoT's inherent distributed nature, the resource constraints and dynamism of its networked participants, as well as the amounts and diverse types of data collected are challenging even the most advanced automated data analysis methods known today. Currently, there is a strong research focus on the centralization of all data in the cloud, processing it according to the paradigm of parallel high-performance computing. However, the resources of devices and sensors at the data generating side might not suffice to transmit all data. For instance, pervasive distributed systems such as wireless sensors networks are highly communication-constrained, as are streaming high throughput applications, or those where data masses are simply too huge to be sent over existing communication lines, like satellite connections. Hence, the IoT requires a new generation of distributed algorithms which are resource-aware and intelligently reduce the amount of data transmitted and processed throughout the analysis chain. This thesis deals with the distributed analysis of vertically partitioned sensor measurements under communication constraints, which is a particularly challenging scenario. Here, not observations are distributed over nodes, but their feature values. The learning of accurate prediction models may require the combination of information from different nodes, necessarily leading to communication. The main question is how to design communication-efficient algorithms for the scenario, while at the same time preserving sufficient accuracy. The first part of the thesis introduces fundamental concepts. An overview of the IoT and its many applications is given, with a special focus on data analysis, the vertically partitioned data scenario, and accompanying research questions. Then, basic notions of machine learning and data mining are introduced. A selection of existing distributed data mining approaches is presented and discussed in more detail. Distributed learning in the vertically partitioned data scenario is then motivated by a smart manufacturing case study. In a hot rolling mill, different machines assess parameters describing the processing of single steel blocks, whose quality should be predicted as early as possible, by analysis of distributed measurements. Each machine creates not single value series, but many of them. Their heterogeneity leads to challenging questions concerning the steps of preprocessing and finding a good representation for learning, for which solutions are proposed. Another problem is that quality information is not given for individual blocks, but charges of blocks. How can we nevertheless predict the quality of individual blocks? Time constraints lead to questions typical for the vertically partitioned data scenario. Which data should be analyzed locally, to match the constraints, and which should be sent to a central server? Learning from aggregated label information is a relatively novel problem in machine learning research. A new algorithm for the task is developed and evaluated, the Learning from Label Proportions by Clustering (LLPC) algorithm. The algorithm's performance is compared to three other state-of-the-art approaches, in terms of accuracy and running time. It can be shown that LLPC achieves results with lower running time, while accuracy is comparable to that of its competitors, or significantly higher. The proposed algorithm comes with many other benefits, like ease of implementation and a small memory footprint. For highly decentralized systems, the Training of Local Models from (Label) Counts (TLMC) algorithm is proposed. The method builds on LLPC, reducing communication by transferring only label counts for batches of observations between nodes. Feasibility of the approach is demonstrated by evaluating the algorithm's performance in the context of traffic flow prediction. It is shown that TLMC is much more communication-efficient than centralization of all data, but that accuracy can nevertheless compete with that of a centrally trained global model. Finally, a communication-efficient distributed algorithm for anomaly detection is proposed, the Vertically Distributed Core Vector Machine (VDCVM). It can be shown that the proposed algorithm communicates up to an order of magnitude less data during learning, in comparison to another state-of-the-art approach, or training a global model by the centralization of all data. Nevertheless, in many relevant cases, the VDCVM achieves similar or even higher accuracy on several controlled and benchmark datasets. A main result of the thesis is that communication-efficient learning is possible in cases where features from different nodes are conditionally independent, given the target value to be predicted. Most efficient are local models, which exchange label information between nodes. In comparison to consensus algorithms, which transmit labels repeatedly, TLMC sends labels only once between nodes. Communication could be even reduced further by learning from counts of labels. In the context of traffic flow prediction, the accuracy achieved is still sufficient in comparison to centralizing all data and training a global model. In the case of anomaly detection, similar results could be achieved by utilizing a sampling approach which draws only as many observations as needed to reach a (1+ε)-approximation of the minimum enclosing ball (MEB). The developed approaches have many applications in communication-constrained settings, in the sectors mentioned above. It has been shown that data can be reduced and learned from before it even enters the cloud. Decentralized processing might thus enable the analysis of big data masses, the more devices are getting connected to the IoT

BOOST THE DSICOVERY OF MRP7/ABCC10 SUBSTRATES AND INHIBITORS: ESTABLISHMENT OF NEW IN VITRO AND IN SILICO MODELS

ATP-binding cassette (ABC) transporters are responsible for the efflux of structurally distinct endo- and xenobiotics energized by ATP hydrolysis. MRP7/ABCC10 belongs to the 10th member of subfamily C and responsible for mediating MDR against a series of chemotherapeutic drugs such as taxanes, epothilones, Vinca alkaloids, anthracyclines and epipodophyllotoxins. Establishment of new in silico and in vitro models for MRP7 substrates/inhibitors prediction Considering the limited knowledge of MRP7, we established a homology model based on bovine MRP1 cryo-EM models. The final model was used for protein global motion analysis and docking analysis. Before docking, potential drug binding pockets were identified and evaluated. Next, MRP7 substrates and inhibitors were docked into drug binding pockets. We found that docked inhibitors and substrates formed separate clusters, from which a substrate binding region and an inhibitor binding region were proposed. This homology protein model enables the docking analysis of potential MRP7 ligands for future studies. Moreover, we established a new SKOV3/MRP7 cell line which exhibits similar drug resistance profile as the previously established HEK/MRP7 cell line. This new cell line is valuable for MRP7 substrates and inhibitors discovery. Last but not the least, we established a novel machine learning model named Mrp7Pred for large-scale MRP7 substrates/inhibitors prediction. The model was also deployed as a web server and is freely available to users in http://www.mrp7pred.com. We successfully identified 2 substrates and 4 inhibitors from 70 FDA-approved drugs using Mrp7Pred. New synthetic agents targeting MRP7 and overcomes MRP7-medited MDR Previously, we identified two synthetic compounds, CMP25 and CP55, as potent ABCB1 and ABCG2 inhibitors. Here we found these two compounds also significantly reversed the MDR mediated by MRP7. Both compounds significantly sensitized MRP7- overexpressing HEK/MRP7 cells to paclitaxel and vincristine. Western blotting indicates that neither CMP25 nor CP55 alters MRP7 expression level. Immunofluorescence showed that the subcellular localization of MRP7 was not altered by these two compounds. However, intracellular accumulation of [3H]-paclitaxel and [3H]-vincristine were significantly increased while the efflux was significantly reduced when co- administered with CMP25 or CP55. Hydrophobic interactions were predicted as the major contributors in stabilizing the drug-protein complex via docking analysis