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"title": "Visual task solution strategies in tree diagrams",
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"abstract": "To be successful, websites must not only contain useful information, but provide that information in a quickly and easily accessible manner. One method of delivering this experience is to design websites that effectively guide users' attention to key information on the page. Grounded in the model of visual hierarchy, this study examines several attributes that can affect users' attention to key information. Using an eye tracking device, users' eye movements were recorded while completing tasks on web pages. The results provide partial support for the model of visual hierarchy and indicate that tracking users' eye movement is an effective method for informing design.",
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"abstract": "To ease the problem of data overloading, it is crucial to understand the user behavior when s/he interacts with online contents, or more specifically, a web page containing several entries for further exploration. We devise to estimate the relevance of an entry to the user goal by observing eye movements as implicit feedback. Specifically, this study proposes a framework that assumes eye movement measures can be used to infer a user¡¦s cognition. A rating task was conducted in which subjects were required to judge whether an image was relevant to a word. Results showed that the total fixation duration and the fixation count can be used to discriminate between the relevant and irrelevant conditions, in contrast, the first fixation duration cannot. In addition, the subjective rating and relevancy manipulation interacted on the total fixation duration. Converging evidence verifies the assumption we have proposed.",
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"fullName": "Fan-Ning Cheng",
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"fullName": "Wei-Guang Teng",
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"abstract": "Nowadays, more and more singers published various songs. There are about 50,000 songs in the internet. The songs are refreshed everyday, so we need a real-time chart of music. In this paper, our study is based on the real-time search charts of music. We studied the algorithm of the charts and analyzed the ranking of the charts. According to the ranking of the charts, we got some special characters. Those characters can not be seen from other music charts,such as selling charts and other week’s charts of music.While, there are also some problems about the realtime charts, like special users of charts. Those problems need us to solve. We will get more impersonality charts after solving those problems.",
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"abstract": "Research advances in network traffic forecasting are continually emerging in the literature. Although the progress in this field, the quality control of traffic forecasts is commonly neglected in previous works. In this paper we analyze the performance of CuSum, Shewhart, and EWMA control charts applied to the residuals of forecasting models fitted to selected traffic samples obtained from real IP networks. Based on numerical and graphical analyses, we verify that CuSum and Shewhart control charts perform worse than EWMA, since they report false alarms for several forecasting processes considered under control. Differently, the EWMA control chart shows efficacy to monitor the different types of studied forecast residuals. Finally, we present a sensitive analysis of the EWMA model with respect to the lambda parameter in order to evaluate its flexibility on different traffic scenarios.",
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"content": "Research advances in network traffic forecasting are continually emerging in the literature. Although the progress in this field, the quality control of traffic forecasts is commonly neglected in previous works. In this paper we analyze the performance of CuSum, Shewhart, and EWMA control charts applied to the residuals of forecasting models fitted to selected traffic samples obtained from real IP networks. Based on numerical and graphical analyses, we verify that CuSum and Shewhart control charts perform worse than EWMA, since they report false alarms for several forecasting processes considered under control. Differently, the EWMA control chart shows efficacy to monitor the different types of studied forecast residuals. Finally, we present a sensitive analysis of the EWMA model with respect to the lambda parameter in order to evaluate its flexibility on different traffic scenarios.",
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"abstract": "Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we employ a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed method achieves an accuracy of 97.0% in the severity classification of CAD.",
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"abstract": "Long-Term person re-identification (LT-reID) exposes extreme challenges because of the longer time gaps between two recording footages where a person is likely to change clothing. There are two types of approaches for LT-reID: biometrics-based approach and data adaptation based approach. The former one is to seek clothing irrelevant biometric features. However, seeking high quality biometric feature is the main concern. The latter one adopts fine-tuning strategy by using data with significant clothing change. However, the performance is compromised when it is applied to cases without clothing change. This work argues that these approaches in fact are not aware of clothing status (i.e., change or no-change) of a pedestrian. Instead, they blindly assume all footages of a pedestrian have different clothes. To tackle this issue, a Regularization via Clothing Status Awareness Network (RCSANet) is proposed to regularize descriptions of a pedestrian by embedding the clothing status awareness. Consequently, the description can be enhanced to maintain the best ID discriminative feature while improving its robustness to real-world LT-reID where both clothing-change case and no-clothing-change case exist. Experiments show that RCSANet performs reasonably well on three LT-reID datasets.",
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"abstract": "Solar energy is one of the cleanest renewable energy sources available. Several studies proposed models for Global Horizontal Irradiance (GHI) forecasting, which showed that deep learning models such as Long Short-Term Memory (LSTM) could be successful at this task. However, these models needed relatively large amounts of training data to reach excellent accuracy, which may be challenging for locations where only limited data are available. This experiment aims to determine the minimum amount of historical data needed by researchers to forecast GHI one hour ahead, using a deep learning LSTM model whilst maintaining an excellent accuracy range measured by nRMSE. To achieve this objective, we trained an LSTM model with different amounts of training data, starting with five years of training data then gradually reducing the amount by one year in each trial until using only one year of training data. The accuracy of the model in terms of nRMSE is reported in each trial. The experiments were conducted using historical GHI and meteorological data from three locations in Saudi Arabia. It is concluded that it is possible for an LSTM model to achieve excellent GHI forecasting accuracy (nRMSE<10%) using only two years of training data.",
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"normalizedAbstract": "The future price prediction of any financial market is a challenging problem that has been in research for decades. From basic pattern recognition to supercomputers running complex algorithms and deep neural networks, the search for a robust prediction of the future price has never stopped. This paper discusses various approaches for financial analysis and the different indicators used widely by traders to place the trades. Over the ten years' worth of daily closing prices, financial reports, and other economic data, several network models are built using simple and multiple linear regression, support vector regression, and artificial neural networks (ANNs). Using such long-term financial data, we perform a detailed investigation on short-term regression techniques to determine the best future price predictor. The paper concludes that ANNs can out-perform other models used and achieve an accuracy of 83% for predicting future prices of EUR/USD.",
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"title": "An Interview-Based User Study on the use of Visualizations for Folder Browsing",
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"abstract": "Hierarchically structured data collections often need to be visualized for the purposes of digital information management and presentation. File browsing, in particular, has an inherent hierarchical structure and plays an important role in the context of Personal Information Management (PIM). A multitude of file browsers are nowadays available, offering different functionalities, while users adopt diverse practices and habits for browsing activities. In this paper, we investigate these aspects to obtain insights into their advantages and disadvantages and suggest solutions in the area of PIM, as well as in other domains employing similar visualization paradigms. The presented study focuses on the two most widespread visualizations used by file browsers, the indented list and zoomable interface paradigms, and assesses their effectiveness for various tasks and contexts, both by exploiting results on existing evaluations on hierarchy visualizations and folder hierarchy visualizations in particular, and by conducting an interview-based user study.",
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"content": "Hierarchically structured data collections often need to be visualized for the purposes of digital information management and presentation. File browsing, in particular, has an inherent hierarchical structure and plays an important role in the context of Personal Information Management (PIM). A multitude of file browsers are nowadays available, offering different functionalities, while users adopt diverse practices and habits for browsing activities. In this paper, we investigate these aspects to obtain insights into their advantages and disadvantages and suggest solutions in the area of PIM, as well as in other domains employing similar visualization paradigms. The presented study focuses on the two most widespread visualizations used by file browsers, the indented list and zoomable interface paradigms, and assesses their effectiveness for various tasks and contexts, both by exploiting results on existing evaluations on hierarchy visualizations and folder hierarchy visualizations in particular, and by conducting an interview-based user study.",
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"fullName": "Maria Golemati",
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"title": "3D Rotation Invariant Features for the Characterization of Molecular Density Maps",
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"abstract": "Cryo electron microscopy produces 3D density maps of large macromolecular structures. An important task lies in the efficient identification of structural similarities between different molecular density maps. Here we construct and test three different types of 3D rotation invariant features for template free similarity detection in molecular density maps. The density map comparison is based on feature vectors that describe the surrounding density distribution for a given map position. We propose Fast Fourier Transform based methods to speed up the computation of feature vectors. Previously, little is known about the discriminative power of rotation invariant features for noisy maps. Here, we test the three feature types with density maps at different noise levels. We assess the performance of our feature vectors by a classification experiment of protein density maps. Our results show that at low noise levels the three types of features perform equally well. However, at high noise levels the features that are constructed by a spherical harmonics decomposition of the density neighborhood is significantly more reliable and outperform the other two feature types, which are based on the moments and intensity histograms of the density distribution.",
"abstracts": [
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"content": "Cryo electron microscopy produces 3D density maps of large macromolecular structures. An important task lies in the efficient identification of structural similarities between different molecular density maps. Here we construct and test three different types of 3D rotation invariant features for template free similarity detection in molecular density maps. The density map comparison is based on feature vectors that describe the surrounding density distribution for a given map position. We propose Fast Fourier Transform based methods to speed up the computation of feature vectors. Previously, little is known about the discriminative power of rotation invariant features for noisy maps. Here, we test the three feature types with density maps at different noise levels. We assess the performance of our feature vectors by a classification experiment of protein density maps. Our results show that at low noise levels the three types of features perform equally well. However, at high noise levels the features that are constructed by a spherical harmonics decomposition of the density neighborhood is significantly more reliable and outperform the other two feature types, which are based on the moments and intensity histograms of the density distribution.",
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"fno": "3885a074",
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"fullName": "Min Xu",
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"fullName": "Shihua Zhang",
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"abstract": "Regional metro lines that connect downtown area and certain suburban area always have some characteristics like long distance and asymmetric passenger distribution, so this kind of regional metro lines would always carry out multirouting modes. On consider of passenger flow distribution and cost-effective operation, some lines will adopt different train running modes. In this paper, the forms of the routing modes and the theory of marshalling modes are studied based on onbuilding Shanghai Metro Line 16, then, operation diagram for the long-short route has been studied. Based on TPM (Train Plan Maker) simulation, the optimal overtaking station, the quantity of operating trains and the requirements of the switchback stations with mixed running mode (including regular running, non-stop running, short route running and part stop running) trains in operation has been analyzed. Finally, an optimal strategy of operation organization has been given.",
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"abstract": "Modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and \"why\" when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.",
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"abstract": "We address the problem of incorporating uncertain location data in the generation of speed profiles for vehicles on roads with multiple lanes. Moving objects' location data can be obtained from different/multiple sources - e.g., GPS on-board the moving objects, roadside sensors, cameras. However, each source has inherent limitations that affect the precision - from pure measurement-errors, to sparsity of their distribution. Incorporating such imprecision is paramount in any query/analytics oriented system that deals with location data. The difficulties multiply when one needs to reason about localization with lane-awareness and attempts to use the location-in-time data to enable effective navigation systems. To tackle this problem, we take a step towards: (a) incorporating uncertainty of the objects' locations into traditional map-matching processes, thereby augmenting them with its impact on different lanes, (b) introducing an information theoretic distance function that can be used to decide when two \"units\" qualify to belong to a same cluster. Our experiments demonstrate that the proposed approach offers a more effective way to generate spatio-temporal clusters with similar speed profiles which, in turn, enables more efficient routes generation.",
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"content": "We address the problem of incorporating uncertain location data in the generation of speed profiles for vehicles on roads with multiple lanes. Moving objects' location data can be obtained from different/multiple sources - e.g., GPS on-board the moving objects, roadside sensors, cameras. However, each source has inherent limitations that affect the precision - from pure measurement-errors, to sparsity of their distribution. Incorporating such imprecision is paramount in any query/analytics oriented system that deals with location data. The difficulties multiply when one needs to reason about localization with lane-awareness and attempts to use the location-in-time data to enable effective navigation systems. To tackle this problem, we take a step towards: (a) incorporating uncertainty of the objects' locations into traditional map-matching processes, thereby augmenting them with its impact on different lanes, (b) introducing an information theoretic distance function that can be used to decide when two \"units\" qualify to belong to a same cluster. Our experiments demonstrate that the proposed approach offers a more effective way to generate spatio-temporal clusters with similar speed profiles which, in turn, enables more efficient routes generation.",
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"abstract": "The Internet-of-Things (IoT) attracts great attention in the past few years. With millions of devices connected to the network, data are generated at an unprecedented speed and the data must be stored efficiently in the database to serve spatial queries. In existing spatial databases that use space-filling curves to organize the data, they store spatial data without considering on-road data distribution. This will introduce unnecessary computation and I/O cost in the service of users' queries about data on the roads. In this paper, we present a Road-Aware Spatial Mapping of data to the storage, or RASM for short, which can be applied in spatial databases for highly efficient storage and query services for moving objects. Usually, a space-filling curve, such as the Hilbert curve, is used to map data in a cell of a geographical area to a segment of linear storage space. However, in a road-network system where data are most distributed and queried along the roads, using a generic square cell as a mapping unit to aggregate data is in conflict with the data use pattern. In RASM, road segment, instead of the cell, is used as the unit of space mapping and data storage so that data requested in a road query can be stored together to enable efficient I/O. Furthermore, a substantial computation may be required to identify mapping units covered in a query in a geometric space. As RASM has grouped data in the road-segment units, one can efficiently found the units covered in a road query, which is usually concerned only about data on a few segments of roads. We implemented a prototype query-serving system using RASM to map data on road segments to a linear space enabled by LevelDB, a widely-used key-value store. Experiment results with real-world traffic data show that with RASM, the road query time can be reduced by up to 43%, and the I/O traffic can be reduced by up to 70%. In the meantime, other queries about geographical regions are well supported in RASM with minimal performance impacts.",
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"content": "Physical quantities referring to angles, like vector direction, color hue etc., are periodic in nature. Due to this periodicity edge detectors proposed for data on the line cannot be used to detect edges in angle-related signals. In this paper we use estimators of circular dispersion to introduce edge detectors for angular signals and discuss their application in edge detection on hue images. Extensions of the notion of quasi-range to circular data are also proposed. These \"circular\" quasi-ranges have good and user-controlled properties as edge detectors on noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using both qualitative and quantitative criteria.",
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"abstract": "The similarity between two histograms has attracted many researchers in various fields. The type of histograms to be matched is often angular such as gradient directions in character images and hue values in color images. The distance between two angular type histograms differs from those of nominal or ordinal type histograms. Alas, conventional distance measures do not distinguish them. Thus, we present a new algorithm to compute the newly proposed distance. We describe how to apply the distance to handwritten character similarity.",
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"content": "The similarity between two histograms has attracted many researchers in various fields. The type of histograms to be matched is often angular such as gradient directions in character images and hue values in color images. The distance between two angular type histograms differs from those of nominal or ordinal type histograms. Alas, conventional distance measures do not distinguish them. Thus, we present a new algorithm to compute the newly proposed distance. We describe how to apply the distance to handwritten character similarity.",
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"abstract": "Color histogram is an important technique for color image database indexing and retrieving. In this paper, traditional color histogram is modified to capture spatial layout information of each color and three types of spatial color histograms are introduced: annular, angular and hybrid color histograms. Experiments show that with a proper trade-off between the granularity in color dimensions and that in spatial dimensions, the performance of these histograms outperforms the traditional color histogram and some existing histogram refinement such as color coherent vector.",
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"fullName": "Rohini K. Srihari",
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"affiliation": "State University of New York At Buffalo",
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"abstract": "A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data with a very large number of objects and dimensions from higher dimensions onto a lower-dimensional space. In visual analytics tasks, the density of the multidimensional clusters can strongly affect how these clusters are perceived. However, this feature can be lost when that dataset is projected into a 2D space, adversely affecting the effectiveness of visual analytics tasks. Thus, it makes sense to preserve, as far as possible, information about the density during the dimensionality reduction. This paper is a study of motion-enhanced cluster perception where the clusters are shown in 2D scatterplots and cluster density is mapped to the motion of the individual constituent points. We consider different types of density-based motion, where the magnitude of the motion is directly related to the density of the clusters. We conducted a series of user studies with large datasets to investigate how motion is a powerful perceptual cue well-suited for grouping or segmenting types during perceptual tasks. We found that the use of motion enabled users to be easily able to distinguish between clusters with different densities. The amount of visual change per unit time was different for the different motions, and we describe the ranges and thresholds for each of them. Specifically, we looked at two projection techniques that output 2D scatterplots for a range of data analysis tasks. We focus on high-dimensional, real-world datasets that might require analyses involving cluster identification, similarity seeking, and cluster ranking tasks. Our results indicate that incorporating density-based motion into visualization analytics systems effectively enables the exploration and analysis of multidimensional datasets.",
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"content": "A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data with a very large number of objects and dimensions from higher dimensions onto a lower-dimensional space. In visual analytics tasks, the density of the multidimensional clusters can strongly affect how these clusters are perceived. However, this feature can be lost when that dataset is projected into a 2D space, adversely affecting the effectiveness of visual analytics tasks. Thus, it makes sense to preserve, as far as possible, information about the density during the dimensionality reduction. This paper is a study of motion-enhanced cluster perception where the clusters are shown in 2D scatterplots and cluster density is mapped to the motion of the individual constituent points. We consider different types of density-based motion, where the magnitude of the motion is directly related to the density of the clusters. We conducted a series of user studies with large datasets to investigate how motion is a powerful perceptual cue well-suited for grouping or segmenting types during perceptual tasks. We found that the use of motion enabled users to be easily able to distinguish between clusters with different densities. The amount of visual change per unit time was different for the different motions, and we describe the ranges and thresholds for each of them. Specifically, we looked at two projection techniques that output 2D scatterplots for a range of data analysis tasks. We focus on high-dimensional, real-world datasets that might require analyses involving cluster identification, similarity seeking, and cluster ranking tasks. Our results indicate that incorporating density-based motion into visualization analytics systems effectively enables the exploration and analysis of multidimensional datasets.",
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"abstract": "We are facing tremendous opportunities and challenges in combining emerging nanotechnology with genomic signal processing techniques in developing faster, smaller, yet more accurate and sensitive biomedical devices in cancer genomics and proteomics. The goal is to better understand the cancer mechanisms at the cellular and even subcellular levels. Nanotechnology has been applied to study the dynamic processes of individual cells, such as cell cycle and cell migration, which can provide rich spatial and temporal phenotype information. In this article, we present the concept of nanoscale biomarkers for cancer genomics and proteomics",
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"title": "Predicting Kinase-Substrate Interactions in Medulloblastoma Subtypes",
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"abstract": "Medulloblastoma (MB), the most common malignant pediatric brain cancer, has four subtypes based on genomics profiling: Wingless (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4. However, subtype heterogeneity and low mutation rates hinder the identification of actionable molecular targets within each group. Studies thus suggest a phosphoproteomics (study of protein phosphorylation), instead of a genomics approach, to better distinguish the MB subgroups. Thus, this research will measure MB's irregular protein activity via protein phosphorylation patterns. Protein kinases phosphorylate proteins on specific amino acids, also known as substrates, to regulate their functions. Given that less than 5% of substrates have annotated kinase associations, it is difficult to identify the kinases in MB which irregularly phosphorylate their substrates in order to determine the most effective kinases for drugs to target. As a result, the publicly-available tool NetworKIN was employed to predict novel kinase-substrate interactions in this study to expand the currently limited annotated interactions. For a given dataset of substrates, NetworKIN will output kinase-substrate predictions, each receiving a confidence score (higher score = higher confidence) ranging from zero to the hundreds. To identify a score threshold for high confidence predictions, NetworKIN was run on the substrates in the Human PhosphoSite Plus Database (PSP), the largest curated database of experimentally verified kinase-substrate interactions in humans. Three statistical thresholding methods were tested and the optimal cutoff score was determined to lie at 5.88. This threshold was applied to the predictions for the MB dataset, resulting in 774 novel high confidence kinase-substrate interactions. To understand how these interactions altered the enriched kinases across the MB subgroups, kinase activity scores before and after adding the predictions were calculated for all 20 samples using the tool Inference of Kinase Activities from Phosphoproteomics. Subgroup classification based on these activity scores revealed that, with the addition of high-confidence interactions to PSP, there are two smaller subgroups of Group 3 and one of those subgroups more closely resembled the Group 4 samples. The alignment of these findings with current literature increases our confidence in the prediction algorithm and elucidates novel phosphorylation patterns to more accurately portray the phosphosignaling pathways driving MB tumorigenesis. Future cancer phosphoproteomics studies can also employ this robust prediction tool to continue expanding our understanding of aberrant kinase-substrate interactions in human diseases.",
"abstracts": [
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"content": "Medulloblastoma (MB), the most common malignant pediatric brain cancer, has four subtypes based on genomics profiling: Wingless (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4. However, subtype heterogeneity and low mutation rates hinder the identification of actionable molecular targets within each group. Studies thus suggest a phosphoproteomics (study of protein phosphorylation), instead of a genomics approach, to better distinguish the MB subgroups. Thus, this research will measure MB's irregular protein activity via protein phosphorylation patterns. Protein kinases phosphorylate proteins on specific amino acids, also known as substrates, to regulate their functions. Given that less than 5% of substrates have annotated kinase associations, it is difficult to identify the kinases in MB which irregularly phosphorylate their substrates in order to determine the most effective kinases for drugs to target. As a result, the publicly-available tool NetworKIN was employed to predict novel kinase-substrate interactions in this study to expand the currently limited annotated interactions. For a given dataset of substrates, NetworKIN will output kinase-substrate predictions, each receiving a confidence score (higher score = higher confidence) ranging from zero to the hundreds. To identify a score threshold for high confidence predictions, NetworKIN was run on the substrates in the Human PhosphoSite Plus Database (PSP), the largest curated database of experimentally verified kinase-substrate interactions in humans. Three statistical thresholding methods were tested and the optimal cutoff score was determined to lie at 5.88. This threshold was applied to the predictions for the MB dataset, resulting in 774 novel high confidence kinase-substrate interactions. To understand how these interactions altered the enriched kinases across the MB subgroups, kinase activity scores before and after adding the predictions were calculated for all 20 samples using the tool Inference of Kinase Activities from Phosphoproteomics. Subgroup classification based on these activity scores revealed that, with the addition of high-confidence interactions to PSP, there are two smaller subgroups of Group 3 and one of those subgroups more closely resembled the Group 4 samples. The alignment of these findings with current literature increases our confidence in the prediction algorithm and elucidates novel phosphorylation patterns to more accurately portray the phosphosignaling pathways driving MB tumorigenesis. Future cancer phosphoproteomics studies can also employ this robust prediction tool to continue expanding our understanding of aberrant kinase-substrate interactions in human diseases.",
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