Deep Learning Prediction Research Articles

CAN-Net: A Multi-hidden Layer Attention Deep Learning Method for Surface Roughness Prediction During Abrasive Belt Grinding of Superalloy with Local Weights

Nickel-based superalloys are widely employed in aerospace due to their excellent high-temperature strength, good oxidation resistance, and hot corrosion resistance. Abrasive belt grinding can effectively solve the problems of excessive residual stress and tool wear during the processing of superalloys. However, due to the grinding process being complex and changeable, and a wide range of affecting factors, the surface roughness prediction of abrasive belt grinding has become a challenging topic. In this study, a CAN-Net multi-hidden layer deep learning prediction model is established. The concatenate path is utilized to fuse local weights to optimize the intermediate weights of network training. To increase the predictability of the model, the attention mechanism is included to distribute the weights of the grinding parameters, and the impact of the attention mechanism on the prediction is then carefully analyzed. The results demonstrate that the CAN-Net network model has outstanding parameter flexibility and prediction accuracy, with accuracy reaching 0.984 and a correlation coefficient of 0.981 between the anticipated value and the true value.

Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

A novel quality control method of time-series ocean wave observation data combining deep-learning prediction and statistical analysis

Quality control (QC) of marine data is a critical aspect in ensuring the usability of oceanic data. In this paper, we propose a novel QC method for time-series ocean wave data, which combines deep learning prediction and statistical analysis. Our method first realizes multi-element LSTM prediction of the time-series ocean wave observation data, capturing both temporal consistency and physical relationships between the multi-element inputs. Then, it applies peak detection on the regional mean difference ratio derived from the predicted and true values of the ocean wave data, and finally labels the anomalous data points based on peak detection results. We conducted experiments on the time-series wave data of four sites from the National Marine Science Center, China, and compared our proposed method with traditional QC method that is currently used for operational marine data quality control, as well as classic anomaly detection models including Isolation Forest and VAE-LSTM. The results show a significant improvement in the Precision, Recall, and F1 Score for erroneous samples using our proposed method. Our proposed method takes advantages of both deep learning and statistical analysis and considers physical correlation of multiple elements of marine data, effectively addressing the problem of erroneous discrimination of abnormal sea conditions in the traditional method, and providing valuable insights for the study of marine time-series observational data.

Deep learning prediction model for central lymph node metastasis in papillary thyroid microcarcinoma based on cytology.

Controversy exists regarding whether patients with low-risk papillary thyroid microcarcinoma (PTMC) should undergo surgery or active surveillance; the inaccuracy of the preoperative clinical lymph node status assessment is one of the primary factors contributing to the controversy. It is imperative to accurately predict the lymph node status of PTMC before surgery. We selected 208 preoperative fine-needle aspiration (FNA) liquid-based preparations of PTMC as our research objects; all of these instances underwent lymph node dissection and, aside from lymph node status, were consistent with low-risk PTMC. We separated them into two groups according to whether the postoperative pathology showed central lymph node metastases. The deep learning model was expected to predict, based on the preoperative thyroid FNA liquid-based preparation, whether PTMC was accompanied by central lymph node metastases. Our deep learning model attained a sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and accuracy of 78.9% (15/19), 73.9% (17/23), 71.4% (15/21), 81.0% (17/21), and 76.2% (32/42), respectively. The area under the receiver operating characteristic curve (value was 0.8503. The predictive performance of the deep learning model was superior to that of the traditional clinical evaluation, and further analysis revealed the cell morphologies that played key roles in model prediction. Our study suggests that the deep learning model based on preoperative thyroid FNA liquid-based preparation is a reliable strategy for predicting central lymph node metastases in thyroid micropapillary carcinoma, and its performance surpasses that of traditional clinical examination.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies.

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

FNReq-Net: A hybrid computational framework for functional and non-functional requirements classification

Requirements classification is a key component of software development life cycle. It enhances our understanding about project requirements, which in turn enables us to effectively identify and mitigate risks that could lead to project failure. Existing requirements classification predictors do not utilize feature selection methods competence in their predictive pipelines and lack in performance. To empower the process of automatic requirements classification, contributions of this paper are manifold. Firstly, it explores the potential of 7 filter-based feature selection techniques and 11 traditional machine learning classifiers. Secondly, for the first time it investigates combined potential of traditional feature selection and 9 diverse types of deep learning predictors. Thirdly, it presents a hybrid computational predictor namely FNReq-Net that reaps combine benefits of traditional feature selection and a novel deep learning predictor based on attention mechanism. Over two public benchmark datasets, large-scale experimental results reveal feature selection not only improves predictive performance of traditional machine learning predictors, but it also improves performance of deep learning predictors. The proposed FNReq-Net predictor outperforms state-of-the-art functional and non-functional requirements classification predictors by 4% and 1% in terms of F1-score over Promise and Promise-exp datasets, respectively.

Infrared Dim and Small Target Detection Based on Background Prediction

Infrared dim and small target detection is a key technology for various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ignore the global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method was proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction was applied for detecting targets. The framework contains a coarse detection module and a fine detection module. In the coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, to further optimize the result, inpainting is utilized to predict the background using the global semantics of images. In this paper, an inpainting algorithm with a mask-aware dynamic filtering module was incorporated into the fine detection stage to estimate the background of the candidate targets. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework has effective detection capability and robustness for complex surroundings.

Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.

Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers. Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers. Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.

Multi-sensor data fusion and bidirectional-temporal attention convolutional network for remaining useful life prediction of rolling bearing

Abstract Remaining useful life (RUL) prediction is crucial in the field of engineering, which can reduce the frequency of accidents and the maintenance cost of machinery. With the increasing complexity of rotating machinery, the data analysis methods based on deep learning have become the mainstream methods of prediction work. However, most of the current RUL prediction methods only use single-sensor data as input, which cannot effectively use multi-sensor data. In addition, as an advanced deep learning prediction method, temporal convolutional network (TCN) only uses the past time information of vibration data to determine the current health status of bearings, while ignoring the importance of future time information of vibration data. To solve the above problems, a bearing RUL prediction method based on multi-sensor data fusion and bidirectional-temporal attention convolutional network (Bi-TACN) is proposed in this paper. In multi-sensor data fusion, multi-sensor data are combined into multi-channel data, and a channel-weighted attention is designed to emphasize the importance of each sensor data. Compared with traditional multi-sensor data fusion, the proposed fusion method allows deep prediction networks to learn more useful feature information from multi-sensor data. Then, Bi-TACN is developed to predict the RUL of bearings. Bi-TACN is mainly composed of the forward TCN block and the backward TCN block, both of which can learn the past and future time information of multi-sensor data simultaneously. Moreover, a temporal attention mechanism is embedded in Bi-TACN to adaptively calibrate the weights of the two TCN blocks, so as to achieve dynamic feature fusion of past and future time information. RUL prediction experiments are carried out through Xi’an Jiao tong University bearing dataset and PHM 2012 bearing dataset respectively. Compared with the advanced prediction methods, the proposed method can accurately predict the RUL of more types of bearings and has low prediction errors.

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.

Deep learning (DL) prediction models hold great promise in the triage of COVID-19. We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.

Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural network (CNN) models to predict host adaptation of swine CoVs. Decomposing of each ORF1ab and Spike sequence was performed with dinucleotide composition representation (DCR) and other traits. The relationship between CoVs from different adaptive hosts was analyzed by unsupervised learning, and CNN models based on DCR of ORF1ab and Spike were built to predict the host adaptation of swine CoVs. The rationality of the models was verified with phylogenetic analysis. Unsupervised learning showed that there is a multiple host adaptation of different swine CoVs. According to the adaptation prediction of CNN models, swine acute diarrhea syndrome CoV (SADS-CoV) and porcine epidemic diarrhea virus (PEDV) are adapted to Chiroptera, swine transmissible gastroenteritis virus (TGEV) is adapted to Carnivora, porcine hemagglutinating encephalomyelitis (PHEV) might be adapted to Primate, Rodent, and Lagomorpha, and porcine deltacoronavirus (PDCoV) might be adapted to Chiroptera, Artiodactyla, and Carnivora. In summary, the DCR trait has been confirmed to be representative for the CoV genome, and the DCR-based deep learning model works well to assess the adaptation of swine CoVs to other mammals. Suiformes might be intermediate hosts for human CoVs and other mammalian CoVs. The present study provides a novel approach to assess the risk of adaptation and transmission to humans and other mammals of swine CoVs.

Research on advertising click-through rate prediction model based on Taobao big data

Ad click-through rate prediction is a key problem in the field of computational advertising. In this paper, LR model, Random Forest model, GDBT model and LightGBM model are used to predict ad click-through rate respectively. In this paper, we adopt the Stacking-based LR and GDBT fusion model and the BP neural network model for deep learning prediction. The experimental results on the real dataset show that the deep learning based BP neural network model performs better than other models.

Mul-DesLSTM: An integrative multi-time granularity deep learning prediction method for urban rail transit short-term passenger flow

It is critical for the management and control of urban rail transit (URT) to be able to predict passenger flow accurately and in real time. Considering that the high-resolution data aggregated by the automatic fare collection (AFC) system is wasted, this paper analyzes the problem of applying a multi-time granularity passenger flow data fusion forecasting process. First, we examine the challenge of constructing a dataset of passenger flow data with different time granularities. Thus, an algorithm is proposed for selecting passenger flow datasets with multi-time granularity. Furthermore, a multi-time granularity dense residual network (Mul-DesLSTM) with a dense residual structure and LSTM (long short-term memory) as the predictor is constructed, inspired by a residual network. Using Mul-DesLSTM, finer-grained passenger flow features can be fused layer by layer while maintaining the accuracy of traditional single-granularity passenger flow predictions. Lastly, Mul-DesLSTM is applied to the URT system of Shanghai, China, and compared with baselines. As a result, the proposed Mul-DesLSTM outperforms the baselines with LSTM as a predictor and state-of-the-art model. When the predicted time granularity is 30 min, compared to the single-time granularity LSTM network, the mean absolute error, root mean square error, and symmetric mean absolute percentage error can be reduced by 51%, 63%, and 15%, respectively. The results can serve as a reference and basis for the operation and management of URT systems.

A highly sensitive multi-channel SPR-PCF based biosensor with deep learning prediction approach

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhancebiosensors.

A hybrid photovoltaic/wind power prediction model based on Time2Vec, WDCNN and BiLSTM

Accurate prediction of photovoltaic (PV)/wind power is an effective solution for the grid stability, reasonable dispatching and power supply reliability. Nowadays, various deep learning prediction methods are developed, most of which are recurrent neural network (RNN)-based and convolutional neural network (CNN)-based. To further improve the prediction performance, a novel hybrid method based on deep CNN with wide first-layer kernels (WDCNN) and bidirection long short-term (BiLSTM) is presented in this paper, in which WDCNN is introduced for large receptive field and useful information extraction, and stacked BiLSTM layers is incorporated to extract temporal correlations of past and future datasets. Besides, Time2Vec is adopted for better feature extraction through decomposing the time series data into non-periodic and periodic components. Several ablation and comparison experiments are carried out with a case study in Yongxing Island, China, and the performance metrics including normalized mean absolute error (NMAE), normalized mean square error (NMSE), normalized root mean square error (NRMSE), and mean absolute scaled error (MASE) confirm the effectiveness and superiority of the proposed model. Compared with individual WDCNN and BiLSTM, the performance of the combined WDCNN-BiLSTM are improved by 5.79%, 6.61%, 3.36%, 5.79% and 2.03%, 3.54%, 1.78%, 2.03% for PV prediction, and 20.38%, 29.37%, 15.96%, 20.38% and 39.12%, 59.68%, 36.50%, 39.12% for wind prediction, respectively. The adoption of Time2Vec further improves the prediction performance by 5.96%, 8.10%, 4.14%, 5.96% for PV prediction, and 4.86%, 9.19%, 4.70%, 4.86% for wind prediction, respectively. The proposed model yields most accurate prediction compared with other competing models. The replacement of CNN with WDCNN improves the prediction accuracy by 6.64%, 8.47%, 4.33%, 6.64% for PV prediction, and 30.59%, 42.78%, 24.35%, 30.59% for wind prediction, respectively. Moreover, the proposed model significantly outperforms compared models in prediction with more random fluctuations, which demonstrates the superiority of the proposed model in mining complex relationships.

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images

This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.

A Multi-Source Intelligent Fusion Assessment Method for Dynamic Construction Risk of Subway Deep Foundation Pit: A Case Study

The construction of a subway deep foundation pit is complex and risky, thus multiple safety risk factors bring great challenges to evaluating the safety status accurately. Advanced monitoring technology equipment could obtain a large number of monitoring data, and how integrating complex and diversified monitoring data to assess the safety risk of foundation pits has become a new problem. Therefore, an intelligent multi-source fusion assessment model is proposed. This model is mainly used for solving risk probability distribution, deep learning, and intelligent prediction of monitoring indicators, and then evaluating safety status by fusing various parameters of multiple indicators. Thus, based on the data of deep learning and the measured multivariate data, the dynamic risk during foundation pit construction can be obtained. Moreover, a typical case study was performed through monitoring and carrying out the risk assessment which is located at the Martyrs’ Lingyuan Station of Jinnan Metro Line R2, China. In this case, the PSO-SVM and LSTM models are used to predict the deformation trend, and the monitoring data is reliable with high precision. After multi-index fusion model calculation, the results show that the foundation pit structure is in a safe state, and the evaluation situation is basically consistent with the site. Consequently, the prediction of the new multi-source intelligent fusion risk assessment method is convincing.

Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

Heart disease is one of the leading causes of death globally, which takes 17.9 million lives each year. The existing heart disease prediction techniques have a gap that does not consider the smoking attributes from the heart disease data. So, the accuracy is based on the limited number of medical data and the deep learning model. The existing deep learning models which use the Recurrent Neural Network (RNN) for heart disease prediction consume more processing and analysing time, mainly due to the delay of data retrieval. This delay causes the prediction process to become slower and leads to a moderate prediction only. The backpropagation of the RNN with an update gate and internal memory to carry the updated data cause a minor data glitch that leads to lower accuracy. Therefore, an efficient heart disease prediction model is very crucial to provide early detection among patients. This research proposes a Heart Disease Risk Prediction Model (HDRPM) with an enhanced RNN to improve the prediction accuracy using Framingham heart disease datasets. The specificity and sensitivity are imposed to improve the quality of the predictions. Sensitivity measure is used for detecting patients with heart disease perfectly and specificity measure is used for detecting patients without the disease perfectly. Besides the accuracy and quality of the prediction problem, the imbalance of minority classes in the dataset occurred in most deep learning prediction fields. This research aims to improve the quality of imbalanced Framingham datasets using Synthetic Minority Over-sampling Technique (SMOTe), which will synthetic instances in a small class to be equalized. The existing RNN model faces vanishing gradients that impede the learning of long data sequences. These gradients that carry information in the RNN cells will become smaller gradually till it minimises the parameter updates and leads to poor learning. For this purpose, the presence of multiple Gated Recurrent Unit (GRU) is used to overcome the vanishing gradients and ensure the hidden layers. The neurons of RNN rapidly cater for the essential information during the training and validation phase of the HDRPM. The integration of multiple GRU with the RNN, operating on the Tensorflow as back-end and Keras as the core for the neural network library has increased the performance of the proposed model. The proposed model provides up to 98.78%, the highest accuracy achieved compared to related previous work, which is a quantum neural network model with 98.57. This HDRPM is expected to significantly contribute to early detection of heart disease patients.

Effects of defects on the transverse mechanical response of unidirectional fibre-reinforced polymers: DEM simulation and deep learning prediction

The presence of defects in composite materials is hardly avoidable during the process of materials manufacturing, which may affect the mechanical behaviour of the material. This paper presents a Representative Volume Element (RVE) based Discrete Element model (DEM) for analysing the effects of defects on the transverse mechanical response of unidirectional (UD) fibre-reinforced polymer (FRP) laminae. Using the DEM model, crack initiation and propagation in defective RVEs with different fibre distributions are analysed and compared. In addition, the effects of the distribution of the defects on stress–strain responses are also investigated. The DEM model shows excellent capabilities in predicting the crack path at failure that is consistent with experimental tests. Based on a data set generated by 1000 DEM simulations, back-propagation deep neural network (DNN) models are developed for a fast determination of crack initiation and instantaneous critical load of the RVEs. The results show that both the initial crack and the critical stress of the laminae can be accurately and efficiently predicted by the data-driven DNN models with consideration of randomly distributed defects.

SMILESynergy: Anticancer drug synergy prediction based on Transformer pre-trained model

The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model-SMILESynergy. First, the drug text data-simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.