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knowledge-based prediction of Network Controllability Robustness

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第10期33卷 5739-5750页

作者： Lou, Yang He, Yaodong Wang, Lin Tsang, Kim Fung Chen, Guanrong City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

Network controllability robustness (CR) reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the CR is determined by attack simulations, which is computationally time-consuming or even infeasible. In this article, an improved method for predicting the network CR is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor;2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.

关键词： Controllability Robustness Image edge detection Correlation knowledge based systems Optimization Neural networks Complex network controllability convolutional neural network (CNN) knowledge-based prediction robustness

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knowledge-based prediction of DNA hydration using hydrated dinucleotides as building blocks

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ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2022年第8期78卷 1032-1045页

作者： Biedermannova, Lada Cerny, Jiri Maly, Michal Nekardova, Michaela Schneider, Bohdan Czech Acad Sci BIOCEV Inst Biotechnol Prumyslova 595 Vestec 25250 Czech Republic

Water plays an important role in stabilizing the structure of DNA and mediating its interactions. Here, the hydration of DNA was analyzed in terms of dinucleotide fragments from an ensemble of 2727 nonredundant DNA chains containing 41 853 dinucleotides and 316 265 associated first-shell water molecules. The dinucleotides were classified into categories based on their 16 sequences and the previously determined structural classes known as nucleotide conformers (NtCs). The construction of hydrated dinucleotide building blocks allowed dinucleotide hydration to be calculated as the probability of water density distributions. Peaks in the water densities, known as hydration sites (HSs), uncovered the interplay between base and sugar-phosphate hydration in the context of sequence and structure. To demonstrate the predictive power of hydrated DNA building blocks, they were then used to predict hydration in an independent set of crystal and NMR structures. In ten tested crystal structures, the positions of predicted HSs and experimental waters were in good agreement (more than 40% were within 0.5 angstrom) and correctly reproduced the known features of DNA hydration, for example the 'spine of hydration' in B-DNA. Therefore, it is proposed that hydrated building blocks can be used to predict DNA hydration in structures solved by NMR and cryo-EM, thus providing a guide to the interpretation of experimental data and computer models. The data for the hydrated building blocks and the predictions are available for browsing and visualization at the website https://***/watna/.

关键词： DNA hydration water dinucleotide fragments knowledge-based prediction WatNA

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knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery

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MEDICAL PHYSICS 2015年第2期42卷 908-917页

作者： Shiraishi, Satomi Tan, Jun Olsen, Lindsey A. Moore, Kevin L. Univ Calif San Diego Dept Radiat Med & Appl Sci La Jolla CA 92093 USA Univ Texas SW Med Ctr Dallas Dept Radiat Oncol Dallas TX 75490 USA Washington Univ Sch Med Dept Radiat Oncol St Louis MO 63110 USA

Purpose: The objective of this work was to develop a comprehensive knowledge-based methodology for predicting achievable dose-volume histograms (DVHs) and highly precise DVH-based quality metrics (QMs) in stereotactic radiosurgery/radiotherapy (SRS/SRT) plans. Accurate QM estimation can identify suboptimal treatment plans and provide target optimization objectives to standardize and improve treatment planning. Methods: Correlating observed dose as it relates to the geometric relationship of organs-at-risk (OARs) to planning target volumes (PTVs) yields mathematical models to predict achievable DVHs. In SRS, DVH-based QMs such as brain V-10Gy (volume receiving 10 Gy or more), gradient measure (GM), and conformity index (CI) are used to evaluate plan quality. This study encompasses 223 linear accelerator-based SRS/SRT treatment plans (SRS plans) using volumetric-modulated arc therapy (VMAT), representing 95% of the institution's VMAT radiosurgery load from the past four and a half years. Unfiltered models that use all available plans for the model training were built for each category with a stratification scheme based on target and OAR characteristics determined emergently through initial modeling process. Model predictive accuracy is measured by the mean and standard deviation of the difference between clinical and predicted QMs, delta QM= QM(clin)-QM(pred), and a coefficient of determination, R-2. For categories with a large number of plans, refined models are constructed by automatic elimination of suspected suboptimal plans from the training set. Using the refined model as a presumed achievable standard, potentially suboptimal plans are identified. predictions of QM improvement are validated via standardized replanning of 20 suspected suboptimal plans based on dosimetric predictions. The significance of the QM improvement is evaluated using the Wilcoxon signed rank test. Results: The most accurate predictions are obtained when plans are stratified based on proxim

关键词： treatment planning SRS dose prediction model DVH knowledge-based prediction

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knowledge-based prediction and Evaluation of Highway Networks Planning

Knowledge-based Prediction and Evaluation of Highway Network...

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The seventh Advanced Forumon Transportation of China(第七届中国交通高层论坛 AFTC'2011)

作者： Lixia Bao ITS R&D Shanghai Urban Construction Design and Research Institute Shanghai 200125 China

ISBN: (纸本)9781622761227

A knowledge-based prediction and evaluation system (KBPEs) for highway networks development planning is put forward, which is a decision support for transportation government. Firstly, the architecture of KBPEs is presented, and then with an example of Weihai city, the paper goes into more detail about how the system works. After analyzing current highway networks of Weihai city and concluding some factors that hold back transportation development, social and economic indexes (population, GDP) are predicted according to the statistics with the historical data. The regression formula among highway networks, national economy and population is presented. Furthermore, future highway networks are reasonably predicted quantitatively and qualitatively so as to meet the requirement of local social and economic development. According to the highway networks development planning of Weihai city, the total highway scale will reach to 2600 km in 2015, 3000 km in 2020 and about 3500 km in 2030. Finally, from the point of technical performance, besides macro-economy and social benefit, the paper gives evaluation of the adaptability of predicted highway networks in Weihai City.

关键词： transport network planning knowledge-based prediction linear regression adaptability evaluation

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HCDP-DELM: Heterogeneous chronic disease prediction with temporal perspective enabled deep extreme learning machine

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knowledge-based SYSTEMS 2024年 284卷

作者： Rehman, Amir Xing, Huanlai Hussain, Mehboob Gulzar, Nighat Khan, Muhammad Adnan Hussain, Abid Mahmood, Sajid Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan Peoples R China Southwest Jiaotong Univ Sch Geosci & Environm Engn Chengdu 611756 Peoples R China Riphah Int Univ Fac Comp Riphah Sch Comp & Innovat Lahore Campus Lahore 54000 Pakistan Gachon Univ Fac Artificial Intelligence & Software Dept Software Seongnam 13120 South Korea Univ Illinois Dept Nephrol Chicago IL 60607 USA

The strain on healthcare systems is growing dramatically due to the significant rise in heterogeneous chronic disease incidence. Even though frequent patient monitoring is complex and expensive, analyzing chronic illness transitions and predicting patients at risk of developing ailments is crucial to healthcare. This paper proposes an innovative prediction framework for heterogeneous chronic disease prediction (HCDP), including hypertension (HT), heart disease (HD), and chronic kidney disease (CKD), and develops a patient symptom-weighted network (PSWN) by projecting the bipartite graph. Hence, PSWN represents the relationship between health conditions for patients with similar illnesses. LSTM was used to capture the temporal relationship between features in the patient network using a complex network theory. Finally, a deep extreme learning machine (DELM) was applied as a classifier to predict heterogeneous chronic disease. Subsequently, the proposed model was evaluated against previous studies, and the result revealed that the proposed model was more accurate. Remarkably, the proposed architecture achieves an accuracy of 95.02% for hypertension, 96.15% for heart disease, and 98.39% for chronic kidney disease prediction. The relevance of the model is also evaluated with the use of the Wilcoxon signed-rank test. As evidenced in this study, the suggested model outperformed previous research and state-of-the-art approaches, suggesting its potential for a pivotal role in the medical decision-making of heterogeneous chronic diagnoses. Furthermore, the HCDP-DELM model contributes to healthcare authorities by establishing a system of smart surveillance and precise forecasting that allows for delayed disease detection and provides insightful potential strategies for developing effective healthcare.

关键词： Heterogeneous chronic disease knowledge-based prediction Deep learning Hypertension Heart disease Chronic kidney disease

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Throughput prediction-based Rate Adaptation for Real-Time Video Streaming over UAVs Networks 9

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9th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Zhou, Tongqing Zhang, Haidong Xu, Ming Chen, Yingwen Natl Univ Def Technol Coll Comp Hefei Peoples R China

ISBN: (纸本)9783319077826;9783319077819

Real-time video streaming is extensively used in UAVs networks for battlefield surveillance, disaster relief, etc. The available throughput of the multi-hop networks varies a lot with the movement of UAVs. To guarantee the video's quality and continuity, rate adaptation mechanism should be used to choose the appropriate transmission rate according to the varying throughput. In this paper, we propose a novel proactive prediction-based adaptation algorithm to avoid disruptions and provide high quality for real-time streaming over UAVs networks. We show that available throughput varies periodically with UAVs' mission-related movement. Then we set a prediction range with the knowledge of periodicity gained from the measurements of a training. The raw prediction is further calibrated with reactive estimation of buffered video time to precisely guide the adaptation. Simulation results show that our scheme maintains a continuous playback with a high quality and significantly shorten the start-up delay compared with two constant bit-rate schemes.

关键词： UAVs networks knowledge-based prediction rate adaptation

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DETECTION OF NATIVE-LIKE MODELS FOR AMINO-ACID-SEQUENCES OF UNKNOWN 3-DIMENSIONAL STRUCTURE IN A DATA-BASE OF KNOWN PROTEIN CONFORMATIONS

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PROTEINS-STRUCTURE FUNCTION AND GENETICS 1992年第3期13卷 258-271页

作者： SIPPL, MJ WEITCKUS, S Institute for General Biology Biochemistry and Biophysics Department of Biochemistry University of Salzburg A-5020 Salzburg Austria

We present an approach which can be used to identify native-like folds in a data base of protein conformations in the absence of any sequence homology to proteins in the data base. The method is based on a knowledge-based force field derived from a set of known protein conformations. A given sequence is mounted on all conformations in the data base and the associated energies are calculated. Using several conformations and sequences from the globin family we show that the native conformation is identified correctly. In fact the resolution of the force field is high enough to discriminate between a native fold and several closely related conformations. We then apply the procedure to several globins of known sequence but unknown three dimensional structure. The homology of these sequences to globins of known structures in the data base ranges from 49 to 17%. With one exception we find that for all globin sequences one of the known globin folds is identified as the most favorable conformation. These results are obtained using a force field derived from a data base devoid of globins of known structure. We briefly discuss useful applications in protein structural research and future development of our approach.

关键词： PROTEIN FOLDING PROTEIN MODELING knowledge-based prediction MOLECULAR FORCE FIELD STATISTICAL MECHANICS, GLOBINS

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ASSEMBLY OF POLYPEPTIDE AND PROTEIN BACKBONE CONFORMATIONS FROM LOW-ENERGY ENSEMBLES OF SHORT FRAGMENTS - DEVELOPMENT OF STRATEGIES AND CONSTRUCTION OF MODELS FOR MYOGLOBIN, LYSOZYME, AND THYMOSIN-BETA-4

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PROTEIN SCIENCE 1992年第5期1卷 625-640页

作者： SIPPL, MJ HENDLICH, M LACKNER, P Department of Biochemistry University of Salzburg Austria.

Recently we developed methods for the construction of knowledge-based mean fields from a data base of known protein structures. As shown previously, this approach can be used to calculate ensembles of probable conformations for short fragments of polypeptide chains. Here we develop procedures for the assembly of short fragments to complete three-dimensional models of polypeptide chains. The amino acid sequence of a given protein is decomposed into all possible overlapping fragments of a given length, and an ensemble of probable conformations is calculated for each fragment. The fragments are assembled to a complete model by choosing appropriate conformations from the individual ensembles and by averaging over equivalent angles. Finally a consistent model is obtained by rebuilding the conformation from the average angles. From the average angles the local variability of the structure can be calculated, which is a useful criterion for the reliability of the model. The procedure is applied to the calculation of the local backbone conformations of myoglobin and lysozyme whose structures have been solved by X-ray analysis and thymosin beta-4, a polypeptide of 43 amino acid residues whose structure was recently investigated by NMR spectroscopy. We demonstrate that substantial fractions of the calculated local backbone conformations are similar to the experimentally determined structures.

关键词： knowledge-based prediction MOLECULAR FORCE FIELD PROTEIN FOLDING PROTEIN MODELING STATISTICAL MECHANICS

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Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

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JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS 2020年第7期21卷 60-69页

作者： Thomas, M. Allan Fu, Yabo Yang, Deshan Washington Univ Dept Radiat Oncol St Louis MO 63110 USA Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX 77030 USA Emory Univ Sch Med Dept Radiat Oncol Atlanta GA USA

Purpose Daily online adaptive plan quality in magnetic resonance imaging guided radiation therapy (MRgRT) is difficult to assess in relation to the fully optimized, high quality plans traditionally established offline. Machine learning prediction models developed in this work are capable of predicting 3D dose distributions, enabling the evaluation of online adaptive plan quality to better inform adaptive decision-making in MRgRT. Methods Artificial neural networks predicted 3D dose distributions from input variables related to patient anatomy, geometry, and target/organ-at-risk relationships in over 300 treatment plans from 53 patients receiving adaptive, linac-based MRgRT for abdominal cancers. The models do not include any beam related variables such as beam angles or fluence and were optimized to balance errors related to raw dose and specific plan quality metrics used to guide daily online adaptive decisions. Results Averaged over all plans, the dose prediction error and the absolute error were 0.1 +/- 3.4 Gy (0.1 +/- 6.2%) and 3.5 +/- 2.4 Gy (6.4 +/- 4.3%) respectively. Plan metric prediction errors were -0.1 +/- 1.5%, -0.5 +/- 2.1%, -0.9 +/- 2.2 Gy, and 0.1 +/- 2.7 Gy for V95, V100, D95, and D(mean)respectively. Plan metric prediction absolute errors were 1.1 +/- 1.1%, 1.5 +/- 1.5%, 1.9 +/- 1.4 Gy, and 2.2 +/- 1.6 Gy. Approximately 10% (25) of the plans studied were clearly identified by the prediction models as inferior quality plans needing further optimization and refinement. Conclusion Machine learning prediction models for treatment plan 3D dose distributions in online adaptive MRgRT were developed and tested. Clinical integration of the models requires minimal effort, producing 3D dose predictions for a new patient's plan using only target and OAR structures as inputs. These models can enable improved workflows for MRgRT through more informed plan optimization and plan quality assessment in real time.

关键词： adaptive radiation therapy dose prediction knowledge-based prediction magnetic resonance image guidance

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Structure-based prediction of binding peptides to MHC class I molecules: Application to a broad range of MHC alleles

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PROTEIN SCIENCE 2000年第9期9卷 1838-1846页

作者： Schueler-Furman, O Altuvia, Y Sette, A Margalit, H Hebrew Univ Jerusalem Hadassah Med Sch Dept Mol Genet & Biotechnol IL-91120 Jerusalem Israel Epimmune Inc San Diego CA 92121 USA

Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.

关键词： knowledge-based prediction MHC class I pairwise potential peptide-protein binding predictive algorithm

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