Method For Prediction Of Response Research Articles

Established a therapeutic response prediction model in metastatic gastric cancer by liquid biopsy

AbstractThis study by Okuno et al. successfully identified eight exo‐miRNAs through exosome‐based discovery and established an exo‐miRNA‐based liquid biopsy assay for predicting therapeutic response in metastatic gastric cancer (mGC). Exosomes, a subpopulation of extracellular vesicles originating from endosomes, play a crucial role in this context. Liquid biopsy, analyzing blood for circulating tumor cells, extracellular vesicles, or cell‐free nucleic acid, has revolutionized cancer diagnosis and monitoring. It significantly contributes to early detection, staging, and relapse detection in various cancers. Numerous studies have highlighted the clinical significance of miRNA and lncRNA within extracellular vesicles. The authors developed a response‐prediction model for chemo‐responsiveness in mGC patients. This study's model predicts responses robustly, demonstrating its potential efficacy in clinical practice. It offers a non‐invasive and accessible method for therapeutic response prediction, crucial for precision medicine in mGC. Successful translation of these findings into clinical applications promises substantial benefits for patient care.

DAPM-CDR: A domain adaptation prompting model for drug response prediction

The rising incidence and mortality rates of cancer present significant challenges to global health. Variations in tumor growth rates and treatment responses have revealed the limitations of traditional therapies, highlighting the urgent need for predictive models for cancer drug responses based on computational methods. Current drug response prediction methods often fall short in accurately predicting responses for rare cancers with limited data. To address these issues, we propose a domain adaptation prompting model for drug response prediction, DAPM-CDR. DAPM-CDR is trained with cancer types with rich response data of cancer cell lines, integrating both common and specific information into prompts through contrastive learning, to transfer knowledge to target tasks that with sparse data on cancer cell line responses, thereby enhancing the generalization capabilities across various cancer types. The experiment results demonstrate that DAPM-CDR outperforms several competitive methods in predicting drug responses of cell lines, particularly excelling with data from rare cancers and showing significant performance enhancements.

Time history response prediction of stochastic SDOF structures through Kriging-NARX modeling and adaptive real-time hybrid simulation

Emulating time history response is a critical benchmark for evaluating the seismic resilience of structures and assessing their reliability in the face of uncertainties. Traditional computational simulations, while commonly favored, often fall short in accurately describing the behavior of intricate structural components. Real-time hybrid simulation (RTHS) offers an effective and efficient experimental technique for replicating the responses of deterministic systems in a cyber-physical manner but encounters challenges when dealing with stochastic systems. This study proposes an innovative application of RTHS for time history response prediction of nonlinear stochastic structures. A cross-validation (CV)-Voronoi sampling strategy is applied to sequentially select uncertain parameter samples for RTHS. Nonlinear autoregressive with exogenous input (NARX) model is identified and updated from RTHS results in a sequential manner. Coefficients of final NARX model are surrogated using the Kriging meta-models to account for given uncertainties. Laboratory tests on single-degree-of-freedom (SDOF) structures with self-centering viscous dampers (SC-VD) were conducted to demonstrate the effectiveness of the proposed approach. Predicted time history responses from the final Kriging-NARX model are further compared and validated. It is demonstrated that the proposed approach provides an effective and efficient method for time history response prediction of stochastic systems especially when accurate numerical models are not available.

A multisource data‐driven monitoring model for assessing concrete dam behavior

AbstractThe pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.

Stability prediction for robotic milling based on tool tip frequency response prediction by considering the interface stiffness of spindle-tool system

The change of tool tip frequency response caused by the posture dependence of robot dynamic is one of the key problems that make it difficult to accurately predict the milling stability of robot. In this paper, a tool tip frequency response prediction method considering the interface stiffness characteristics of spindle-tool system is proposed for stability prediction of robotic milling under the condition of posture variation. Firstly, the interface stiffness models of spindle-toolholder, toolholder-spring clip and spring clip-tool are established based on Yoshimura's unit area method. Then, The dynamics model for the robot body and the interface stiffness models for spindle-tool system are imported into the finite element analysis model of the spindle system, so that the prediction of the tool tip frequency response is realized by harmonic response analysis. Compared with the experimental results, the maximum error of the natural frequency was not more than 2 %, and the maximum error of the amplitude was not more than 12 %. Finally, the 2 DOF robot milling stability prediction model is established. Then the robot milling chatter is predicted considering redundant degrees of freedom from the perspective of regenerative chatter prediction theory, and the accuracy of prediction results is verified by milling experiment.

Abstract LB434: Rapid and reproducible expansion of rare tumor cells: The R3CE platform for personalized medicine

Abstract We have developed a novel single-cell derived 3D organoid culture platform, Rapid, Reproducible, Rare Cell 3D Expansion (R3CE), capable of generating 3D organoids from solid tumors and normal tissues obtained via surgical tissues and needle biopsies within one week. This method demonstrates over 90% success in banking for at least two passages with over 106 cells across various cancer types, including breast cancer, colorectal cancer, hepatoma cancer, and ovarian cancer. The organoids maintain high fidelity to original tissues, as evidenced by H&E staining, WES, and protein marker analysis across generations. Notably, the R3CE platform can also cultivate circulating tumor cells (CTCs) from peripheral blood. The drug sensitivity profiles of RCE-cultured CTCs closely mirrored clinical outcomes in breast cancer patients. For a patient with stage IV HER2-positive breast cancer, our real-time assay results led to an adjustment in treatment from lapatinib/5-FU to lapatinib/Trastuzumab emtansine (TDM-1), which resulted in substantial clinical improvement. To our knowledge, the R3CE platform is the first 3D organoid system that does not require aggregation or 3D scaffold such as matrigel system. These advancements position the R3CE platform as a promising tool for personalized medicine, offering a rapid and accurate method for drug screening and therapeutic response prediction to guide clinical decision-making in cancer treatment. Citation Format: Wen-Hung Kuo, Yen-Jang Huang, Jia-Yang Chen, Yi-Chun Wu, Chia-Chun Chen, Mark D. Pegram, Ying-Chih Chang. Rapid and reproducible expansion of rare tumor cells: The R3CE platform for personalized medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB434.

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.

Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

Abstract 3660: Plasma cell free DNA hydroxymethylation profiling reveals anti-PD1 treatment response and resistance biology in non-small cell lung cancer

Abstract Background: Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable anti-tumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection. Methods: 151 blood samples were collected from 31 non-small cell lung cancer (NSCLC) patients before therapy start and at multiple timepoints whilst on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively. Results: Comparison of on-treatment timepoint to matched pre-treatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responders, as judged by RECIST, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as IFNγ and IFNα response, inflammatory response, and TNFα signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. The Molecular Response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early, starting with the first cycle of treatment. Comparison of pre-treatment plasma samples revealed that anti-PD-1 treatment response- and resistance-associated genes can be captured by 5hmC profiling of plasma-derived cfDNA. Conclusions: These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma samples, offering a novel method for non-invasive prediction and monitoring of immunotherapy response in NSCLC. Citation Format: Gulfem D. Guler, Yuhong Ning, Ceyda Coruh, Tierney Phillips, Maryam Nabiyouni, Kyle Hazen, Aaron Scott, Wayne Volkmuth, Samuel Levy. Plasma cell free DNA hydroxymethylation profiling reveals anti-PD1 treatment response and resistance biology in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3660.

Vibration response prediction and vibration safety assessment method for building structures around viaducts under the action of high-speed railway trains

Concerning the cumulative damage of building structures caused by the long-term effects from high-speed railway train loads and various environmental impacts, and to ensure the service safety of building structures around viaducts, this paper establishes a vibration safety assessment method for the entire life cycle of building structures with less workload and good applicability, while ensuring the accuracy and effectiveness of the prediction of vibration response of building structures. Firstly, the vibration source function is formed based on the mechanical model of the mobile vibration source of high-speed railway trains, and the vibration wave of high-speed railway trains is numerically simulated. Then the time and frequency characteristics of the simulated high-speed railway train vibration waves are verified through on-site monitoring tests. In addition, the peak particle vibration velocity attenuation formula for the foundation site of the viaduct is derived by the dimension analysis method, and it is used to correct the attenuation characteristics of the simulated high-speed railway train vibration waves. Finally, based on the response spectrum method to realize the prediction and assessment of the overall vibration response of the building structures, and using the cumulative damage coefficient to reflect the cumulative degree of the service damage of the building structure, a vibration safety assessment method for the whole lifecycle of the building structure is proposed. The method overcomes the shortcoming that vibration velocity does not reflect the impact of service damage on the vibration safety of building structures, and avoids neglecting localized vibration damage, and is therefore useful for the rapid, overall vibration safety assessment of any long-term service building structure in a viaduct site.

A seismic response prediction method based on a self-optimized Bayesian Bi-LSTM mixed network for high-speed railway track-bridge system

A seismic response prediction method based on a self-optimized Bayesian Bi-LSTM mixed network for high-speed railway track-bridge system

A candidate method for prediction of the non-stationary response of strongly nonlinear systems under wide-band noise excitation

This paper expands the application of the RBFNN method to the prediction of non-stationary responses in strongly nonlinear systems subjected to wide-band noises (WBNs) excitation. Specifically, the SAM is initially employed to deduce the averaged Fokker–Planck–Kolmogorov (FPK) equation. The non-stationary responses of the energy of the system under WBNs are modeled as a one-dimensional Markov diffusion process. This yields a one-dimensional FPK equation that governs the NSRs of the system subject to WBNs. Subsequently, solving the corresponding FPK equation and obtaining the system’s NSRs are accomplished with the RBFNN method. Furthermore, four indicative cases exposed to WBNs are studied to verify the proposed scheme. Comparisons with simulations demonstrate the accuracy and efficiency of the developed technique.

Seismic response prediction of a damped structure based on data-driven machine learning methods

Damping technology has been widely used because of its good vibration control effect. However, due to the strong nonlinearity of the added dampers, accurately predicting the seismic response of damped structures remains a challenge. This study investigates the application of interpretable machine learning (ML)-based and deep learning-based approaches to the prediction of the maximum inter-storey displacement of a damped structure. A comprehensive database consisting of 13,855 structural responses to ground motions was collected. Seven traditional interpretable ML algorithms including random forest and extreme gradient boosting (XGBoost), a convolutional neural network based on large receptive field, and seismic wave transformer (SWT) model based on a transformer network were developed. The predictions show that the error of the SWT based on unsupervised feature extraction is reduced by 50.90% compared with that of the optimal XGBoost in ensemble learning. Although the SWT has the highest global accuracy, XGBoost is found to have a smaller error when the structure is in a linear state with peak ground acceleration as partition index, so an aggregation model (AM)-based structural response prediction method was also proposed. The accuracy of the AM improved by 27.95% compared with that of the SWT. In contrast with other ML models, the proposed AM is more advantageous in terms of computational efficiency and accuracy.

GPDRP: a multimodal framework for drug response prediction with graph transformer

BackgroundIn the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of molecules. Additionally, they ignore gene pathway-specific combinatorial implication.ResultsIn this study, we propose drug Graph and gene Pathway based Drug response prediction method (GPDRP), a new multimodal deep learning model for predicting drug responses based on drug molecular graphs and gene pathway activity. In GPDRP, drugs are represented by molecular graphs, while cell lines are described by gene pathway activity scores. The model separately learns these two types of data using Graph Neural Networks (GNN) with Graph Transformers and deep neural networks. Predictions are subsequently made through fully connected layers.ConclusionsOur results indicate that Graph Transformer-based model delivers superior performance. We apply GPDRP on hundreds of cancer cell lines’ bulk RNA-sequencing data, and it outperforms some recently published models. Furthermore, the generalizability and applicability of GPDRP are demonstrated through its predictions on unknown drug-cell line pairs and xenografts. This underscores the interpretability achieved by incorporating gene pathways.

Chronicle knowledge-based multi-level response prediction for predictive control by forest models in process industry

The control output response prediction is confirmed to be crucial for predictive control in process industries. The data-driven approaches that adopt system-independent predictors for output response predictions of multiple controlled variables are promising due to their easy implementation using available open-source models. However, the intrinsic knowledge among controlled variables is ignored, for it cannot be fed into the data-driven models directly, including control events, control antecedents, and temporal constraint information, which inevitably limits the predictive performance. This study proposes a hybrid control output response prediction method embedded with chronicle knowledge and a data-driven model for predictive control. It starts from the knowledge discovery and instance pool establishment by symbolic techniques. Control intrinsic knowledge extracted from the instance pool will be integrated into the modeling of forest model-based predictors, where the knowledge will be leveraged for predictor structure designing and temporal constraints estimation. In this way, tailored predictors with multi-level structures will be devised to capture the intrinsic correlation of controlled variables with different working levels. The control output response predictors will finally be introduced into the design of predictive controllers to make multi-output response predictions. The experimental results show the superiority of the proposed method over baselines in the prediction accuracy as well as the capability of set-point tracking and disturbance rejection.

Refined self-attention mechanism based real-time structural response prediction method under seismic action

Accurate prediction of structural response under earthquake is of great significance for structural damage and performance evaluation. In order to improve the efficiency of structure time history response prediction, this paper proposes a novel SeisFormer model based on the self-attention mechanism and deep learning technology. Through autoregressive prediction, the SeisFormer can achieve real-time prediction of the response time histories of a large number of nodes in the structure under seismic action and can effectively solve the problem of data scarcity. Four case studies are performed to verify the accuracy and efficiency of the proposed methodology, including validation on datasets obtained from elastoplastic seismic analysis of a single-story structure, a three-story structure, and an eleven-story structure, and measured data of a shaking table test model. In addition, this paper further studies the prediction accuracy of the SeisFormer through ablation experiments and comparative experiments. The experimental results show that the SeisFormer can accurately predict the acceleration, velocity, and displacement time histories of numerous nodes in the structure. The prediction accuracy outperforms the LSTM model, and the prediction speed is 193–109,824 times faster than finite element method. Furthermore, with data augmentation through autoregressive prediction, the SeisFormer model can achieve efficient and accurate predictions when training data is exceptionally scarce, enabling engineering applications.

Transformer based on the prediction of psoriasis severity treatment response

Psoriasis, a chronic inflammatory skin disease, must be continuously monitored to prevent recurrence and increase treatment effectiveness. However, time-series analysis studies considering the chronic characteristics of psoriasis are rare. The psoriasis area and severity index (PASI) score, a therapeutic decision-making index for psoriasis, has limitations in detecting disease variability during treatment. For successful treatment, an imaging-based approach that can intuitively observe lesion changes is required. We propose a novel deep learning-based evaluation method that identifies psoriasis severity characteristics and predicts changes in severity during treatment. The proposed method consists of two steps: extraction of deep features of psoriasis and prediction of disease severity in time series using the deep features. The proposed deep-feature model extracted global and deformable convolution layers V2-based local features using the hierarchical information of RegNetY-1.6G and fused the features to predict disease severity. After severity classification using fusion features, the f1-score was 0.92. Next, we constructed a short-term time-series disease dataset and extracted deep features using the proposed model. Among six time-series analysis models (LSTM, GRU, GRU-FCN, ResNet, InceptionTime, and Transformer), Transformer demonstrated the best prediction performance, with a root mean squared log error (RMSLE) of 0.44 and a symmetric mean absolute percentage error (SMAPE) of 0.34. From the Student’s t-test and regression analysis, the predictions for reducing disease severity for each patient and body part were similar to the actual values. Our study verified the possibility of tracking dynamic changes in the disease and personalized treatment using a psoriasis treatment response prediction method.

An analytical method for evaluating the structural resistance of plate structures under in-plane impact load in ship-side collision scenarios

Ship collision accidents occur occasionally and it is of great significance to develop rapid prediction method for structural response. This paper proposes a new analytical method to predict the crushing resistance of the deck and bulkhead under head-on and oblique collision scenarios by a raked bow. The structural deformation modes of them are studied firstly through the numerical simulations and the analytical models are then established, respectively. In the analytical model, the deck structure is divided into four types of components and corresponding formulas are derived based on the upper-bound theorem of plastic deformation analysis. On the basis of theoretical analysis and statistical studies, a semi-analytical formula is also developed for predicting the structural resistance of the bulkhead under in-plane impact loads. The analytical method developed in this paper considers the damage evolution in different collision stages and the coupling effects from stiffeners. Through the comparative verification of multiple collision scenarios, the results of the newly proposed analytical method and semi-analytical formula are in good agreement with the numerical results. The research outcomes could highly benefit the engineering application for the damage assessment of plate structures in the case of head-on and oblique collision.

A mode selection procedure for a seismic response prediction method based on microtremor measurements

A novel mode selection procedure is proposed for a seismic response prediction method based on microtremor measurements. The prediction method utilizes modal properties of limited modes to model the objective building. The modal properties are determined through microtremor measurements, and hence, modeling does not require design documents. The prediction method utilizes the identified mode shapes to approximate the corresponding participation factors. However, the approximation results might significantly vary after incorporating a new mode. Accordingly, if the incorporated mode is highly linearly relevant to the previously used modes, the approximation accuracy sharply decreases. This results in a less accurate prediction of seismic responses. Therefore, this paper proposes a mode selection procedure for the prediction method. The effectiveness of the procedure is validated through a numerical simulation. Further, the simulation results indicate that using all known modes in the prediction method might significantly decrease the prediction accuracy compared with using selected modes.

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures, including post-earthquake damage assessment, structural health monitoring, and seismic resilience assessment of buildings. To improve the accuracy and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-based real-time structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy. The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model, thereby enabling higher-precision predictions. Experiments were conducted on a four-story masonry structure, an eleven-story reinforced concrete irregular structure, and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method. In addition, the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study. Furthermore, by conducting a comparative experiment, the impact of the range of seismic wave amplitudes on the prediction accuracy was studied. The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.