Regularization Approach Research Articles

Nonlinear acoustic echo cancellation using low-complexity low-rank recursive least-squares algorithms

Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity.

Lagrangian Split-Step Method for Viscoelastic Flows.

This research addresses and resolves current challenges in meshless Lagrangian methods for simulating viscoelastic materials. A split-step scheme, or pressure Poisson reformulation of the Navier-Stokes equations, is introduced for incompressible viscoelastic flows in a Lagrangian context. The Lagrangian differencing dynamics (LDD) method, which is a thoroughly validated Lagrangian method for Newtonian and non-Newtonian incompressible flows, is extended to solve the introduced split-step scheme to simulate viscoelastic flows based on the Oldroyd-B constitutive model. To validate and evaluate the new method's capabilities, the following benchmarks were used: lid-driven cavity flow, droplet impact response, 4:1 planar sudden contraction, and die swelling. These findings highlight the LDD method's effectiveness in accurately simulating viscoelastic flows and capturing large deformations and memory effects. Even though the extra stress was directly modeled without any regularization approach, the method produced stable simulations for high Weissenberg numbers. The stability and performance of the the Lagrangian numerics for complex temporal evolution of material properties and stress responses encourage its use for industrial problems dealing with polymers.

Multi-source partial multi-label learning via tensor decomposition and nonconvex regularization

Multi-source partial multi-label learning (MSPMLL) aims to learn a multi-label classifier from training examples with multi-source features and multiple labels. Most of the existing MSPMLL models tackle each source independently to derive classifiers. Those methods cannot efficiently reveal the intrinsic structure and relationships among instances across different sources, nor deal with inconsistent labels caused by noise. To address those challenges, this paper proposes a tensor decomposition and nonconvex regularization approach to multi-source partial multi-label learning, nominated as MSPML-TLR. In this model, a hypergraph based affinity graph is constructed from each of multi-source data in the feature space. A tensor representation of multi-source multi-label data is proposed based on the affinity graphs. A multi-linear projection is learned to map the feature affinity space to a latent ground-truth label space via tensor robust principal component analysis. Furthermore, a notion called the parameterized tensorial logarithmic rank is designed to approximate the rank of the multi-linear projection and the latent ground-truth label tensor. Moreover, a hypergraph-based Laplacian regularization is imposed on each frontal slice of the latent ground-truth label tensor to preserve the topological structure of instances across multi-source feature spaces. An efficient algorithm is designed to implement MSPML-TLR and the convergence of the algorithm is rigorously proven. Extensive experiments on various real-world benchmark datasets demonstrate that the proposed model outperforms state-of-the-art methods in multi-label classification tasks.

A new adaptive elastic loss for robust unsupervised feature selection

The sparse-based unsupervised dimension reduction technique has been demonstrated to be an effective tool for dealing with high-dimensional data. Among them, the sparse regularization technique is used to introduce the prior knowledge of the model and helps unsupervised dimension reduction algorithms to complete the feature selection. However, the sparse regularization only considers the sparsity of representation. It ignores the group effect of variables among which the pairwise correlations are very high. To solve these issues, we present a novel regularization approach based on adaptive elastic loss in this study. Then, utilizing the regularization method, an efficient unsupervised feature selection methodology is given. Additionally, we devise an efficient alternative optimization algorithm to resolve the complex optimization issue of our proposed method and conduct a theoretical analysis of its computational complexity and convergence. Eventually, we conduct comprehensive experiments to test the validity of our proposed method, and real-world data and a simulation study demonstrate that our proposed method outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering and classification.

A multiclass short-term lightning predictor for the Amazon region using ground-based meteorological data and artificial neural network

Lightning prediction is a complex task due to the intricate nature of this phenomenon. Various sectors of society, including aviation and civil defense, can benefit from accurate lightning occurrence information. Methodologies that can predict lightning occurrence are crucial to minimize the issues related to this phenomenon. In this work, we propose a novel approach using the machine learning model called Artificial Neural Network (ANN) to predict lightning occurrence one hour ahead. We use ground-based meteorological data from the National Institute of Meteorology (INMET) and the number of lightning per hour to feed the ANN model. We used a lightning dataset of six years of observation (2015–2020) from GLD360 to train, validate, and test machine learning algorithm models. A model was developed for each weather station. We use data from 24 stations distributed throughout the Brazilian Legal Amazon region. The multiclass predictor was based on three categories: no lightning, low-intensity, and high-intensity of lightning. We also developed a solution comprising two cascade ANN models, each performing binary prediction. The first predicts the occurrence or no occurrence of lightning, and the second predicts the intensity: low or high. The cascade approach showed superior performance compared to the regular approach.

Using Convolutional Neural Networks in Installation Analysis of Lazy-Wave Flexible Risers

Abstract The design phase of offshore installation projects is supported by numerical simulations. These analyses aim to evaluate the mechanical behavior of the equipment involved, such as vessels and flexible pipes, during that operation. Therefore, a common approach is to take the ocean wave loads modeled as deterministic ones (or regular wave approach), which is a simplification that, on the one hand, allows low computational cost, but, on the other one, lacks the representation of the actual behavior of the wave loads, usually better represented by means of an irregular wave modeling. In the way of searching for an irregular wave analysis procedure to be used in the daily design of lazy-wave riser installation analyses, this work proposes an artificial neural network (ANN)-based approach. The proposed model aims to achieve it by training a convolutional neural network (CNN) fed by generated data from short-length finite element-based numerical simulations. This surrogate model can predict quite well the pipe's top tension and approximately the axial tension in the touchdown zone (TDZ) for different configuration stages during the riser's installation operation. Moreover, the proposed model works for different environmental scenarios, which boosts the computational simulation time reduction in this phase of riser design.

Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization

We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination, facilitating the integration of l1 regularization into the SVR method, thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires, benchmarking against 15 machine learning models, including state-of-the-art techniques, such as Recurrent Trend Predictive Neural Network (rTPNN), Long-Short Term Memory (LSTM), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire, enabling early preventive measures. Notably, it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly, it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR, facilitated by flattening-samples based augmented regularization, allows for high detection performance even with smaller training sets.

Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2

Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.

Peak response regularization for localization

Deep convolutional neural networks approaches often assume that the feature response has a Gaussian distribution with target-centered peak response, which can be used to guide the target location and classification. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produces sub-peaks on the tracking response map and causes model drift. In this paper, we propose a feature response regularization approach for sub-peak response suppression and peak response enforcement and aim to handle progressive interference systematically. Our approach, referred to as Peak Response Regularization (PRR), applies simple-yet-efficient method to aggregate and align discriminative features, which convert local extremal response in discrete feature space to extremal response in continuous space, which enforces the localization and representation capability of convolutional features. Experiments on human pose detection, object detection, object tracking, and image classification demonstrate that PRR improves the performance of image tasks with a negligible computational cost.

Speech Emotion Recognition Incorporating Relative Difficulty and Labeling Reliability.

Emotions in speech are expressed in various ways, and the speech emotion recognition (SER) model may perform poorly on unseen corpora that contain different emotional factors from those expressed in training databases. To construct an SER model robust to unseen corpora, regularization approaches or metric losses have been studied. In this paper, we propose an SER method that incorporates relative difficulty and labeling reliability of each training sample. Inspired by the Proxy-Anchor loss, we propose a novel loss function which gives higher gradients to the samples for which the emotion labels are more difficult to estimate among those in the given minibatch. Since the annotators may label the emotion based on the emotional expression which resides in the conversational context or other modality but is not apparent in the given speech utterance, some of the emotional labels may not be reliable and these unreliable labels may affect the proposed loss function more severely. In this regard, we propose to apply label smoothing for the samples misclassified by a pre-trained SER model. Experimental results showed that the performance of the SER on unseen corpora was improved by adopting the proposed loss function with label smoothing on the misclassified data.

Consensus Subspace Graph Regularization based on prior information for multiplex network clustering

Multiplex network clustering can identify the common cluster structure shared by all layers, which is of great significance for downstream research such as personalized recommendations of social relationships and the mining of social media communication behaviors. Traditional multiplex network clustering methods rely only on the topological structure and are suitable for networks with clear structures. However, real-world networks often suffer from sparse connectivity and noisy edges. In addition, the current methods based on prior information only use the obtained consensus prior information as a preprocessing method for multiplex networks, and do not fully utilize and integrate the prior information in the clustering process, resulting in a low accuracy. To solve the problem of insufficient utilization of prior information, we propose a Consensus Subspace Graph Regularization (CSGR) approach for multiplex network clustering, which integrates topological information with the consensus prior information of a network. We first construct the consensus prior matrix of a network by a non-overlapping greedy search method, which represents a subset of the network that consists of nodes with high edge density. Then we construct a graph regularization term, which encodes the consensus prior information, to optimize the generation of a joint low-dimensional representation within the consensus subspace for the entire multiplex network. Finally, we employ the consensus prior matrix to denoise each network layer, thereby yielding a precise low-dimensional representation of each layer for consensus subspace fusion. In addition, comprehensive experiments have demonstrated that CSGR improves the clustering accuracy of real-world networks by an average of 1.56%.

An accelerated inexact Newton-type regularizing algorithm for ill-posed operator equations

We propose and analyze a new iterative regularization approach, called IN-SETPG, for efficiently solving nonlinear ill-posed operator equations in the Hilbert-space setting. IN-SETPG consists of an outer iteration and an inner iteration. The outer iteration is terminated by the discrepancy principle and consists of an inexact Newton regularization method, while the inner iteration is performed by a sequential subspace optimization method based on the two-point gradient iteration. The key idea behind IN-SETPG is that, unlike the standard Landweber method, it uses multiple search directions per iteration in combination with an adaptive step size in order to reduce the total number of iterations. The regularization property of IN-SETPG has been established, i.e., the iterate converges to a solution of the nonlinear problem with exact data when the noise level tends to zero. Various numerical experiments are presented to demonstrate that, compared with the original inexact Newton iteration, IN-SETPG can achieve better reconstruction results and remarkable acceleration.

Regularized relativistic corrections for polyelectronic and polyatomic systems with explicitly correlated Gaussians.

Drachmann's regularization approach is implemented for floating explicitly correlated Gaussians (fECGs) and molecular systems. Earlier applications of drachmannized relativistic corrections for molecular systems were hindered due to the unknown analytic matrix elements of 1/rix1/rjy-type operators with fECGs. In the present work, one of the 1/r factors is approximated by a linear combination of Gaussians, which results in calculable integrals. The numerical approach is found to be precise and robust over a range of molecular systems and nuclear configurations, and thus, it opens the route toward an automated evaluation of high-precision relativistic corrections over potential energy surfaces of polyatomic systems. Furthermore, the newly developed integration approach makes it possible to construct the matrix representation of the square of the electronic Hamiltonian relevant for energy lower-bound as well as time-dependent computations of molecular systems with a flexible and high-precision fECG basis representation.

Multi-Dimensional Manifolds Consistency Regularization for semi-supervised remote sensing semantic segmentation

Semi-supervised semantic segmentation in remote sensing is critical for urban planning, environmental monitoring and disaster response. The high cost and time required for high-quality data annotation limits its wider application. Traditional semi-supervised deep learning methods, which operate in a single dimension, limit model robustness and generalization. Our study addresses this issue by proposing an effective semi-supervised learning method. This method improves model robustness and generalization in remote sensing semantic segmentation. We introduce the Multi-Dimensional Manifolds Consistency Regularization (MDMCR) approach. It applies multi-dimensional perturbations to input images and features, expanding the sample library and improving learning efficiency. Our method has been rigorously tested on various datasets. With only 1/8 of the data labeled, it achieved mean Intersection over Union (mIoU) scores of 74.48% on ISPRS Vaihingen and 78.80% on Potsdam. With only 5% labeled data, it reached 49.93% mIoU on DeepGlobe Roads and 57.90% on Massachusetts Roads. These results show the superiority of our method over existing techniques.

An adaptive mesh scheme of the lattice spring model based on geometrical continuity

An adaptive mesh scheme is introduced for the lattice spring model (LSM), where the original triangular cells are subdivided into a set of smaller triangular cells. The scheme is based on geometrical continuity at the heterogeneous mesh boundary, where the refined grid cells intersect the original cell edge. The LSM simulations on the refined grid show a superior computational efficiency to the uniform grid. Each subdivision reduces the original cell edges by a factor of two. The refinement procedure was recursively applied ten times before any marked loss in accuracy was observed. The accuracy of the adaptive model is on par with a regular grid approach. More specifically, the characteristics of fracture cavity are comparable with a uniform grid of the same mesh density as the smallest cells in the adaptive approach. The fracture criterion such as J-integral, the elastic energy of the grid and potential energy change due to fracture growth and strain loading agree well with the theory of a mode I fracture, which enables simulations of process such as sub-critical fracture with a wide dynamic range.

Robust Algorithms for the Analysis of Fast-Field-Cycling Nuclear Magnetic Resonance Dispersion Curves

Fast-Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful, non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a model-free approach, representing the NMRD profile R1 as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analyzing NMRD profiles. These include (1) MF-UPen, utilizing locally adapted L2 regularization; (2) MF-L1, based on L1 penalties; and (3) a hybrid approach combining locally adapted L2 and global L1 penalties. Each method’s regularization parameters are determined automatically according to the Balancing and Uniform Penalty principles. Our contributions include the implementation and experimental validation of the MF-UPen and MF-MUPen algorithms, and the development of a “dispersion analysis” technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios.

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

AbstractIn smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.

A Factor‐Augmented New Keynesian Phillips Curve for the European Union Countries

AbstractIn this paper, a factor‐augmented version of the New Keynesian Phillips curve (NKPC) is assessed using a data set comprised of a large panel of European Union (EU) member countries. The factor‐augmentation is natural given that country‐level inflation rates are highly co‐moving. The presence of unattended common factors is important because it raises the issue of omitted variables bias, as the real marginal cost, which is a regressor of the NKPC, is likely to load on the same factors as inflation. One possibility here is to employ the regular instrumental variables approach. However, if the external instruments load on the same factors as the error term of the NKPC, the instruments would be invalid and the results would therefore likely be misleading. Motivated by this last observation, the present paper proposes a new estimator of the NKPC that allows for very general forms of factor dependencies and endogeneity. Our results provide evidence in support of the NKPC, but only after the presence of common factors has been appropriately accounted for.

High dimensional variable selection through group Lasso for multiple function‐on‐function linear regression: A case study in PM10 monitoring

SummaryAnalyzing the effect of chemical and local meteorological variables over the behaviour in concentrations in the Abruzzo region (Italy), with the objective of forecasting and controlling air quality, motivates the current work. Given that the available data are curves that represent the day‐to‐day variations, a multiple function‐on‐function linear regression (MFFLR) model is considered. By assuming the Karhunen‐Loève expansion, MFFLR model can be reduced to a classical linear regression model for each principal component of the functional response in terms of all principal components (PCs) of the functional predictors. In this sense, a regularization approach for functional principal component regression based on the merge of functional data analysis with group Lasso is proposed. This novel methodology allows to estimate the model and, simultaneously, select those relevant functional predictors with the functional response, where each functional independent variable is represented by a group of input variables derived by the PCs.

Abstract PS06-07: Liquid biopsy determination of HER2 status in breast cancer: results from a novel epigenomic platform

Abstract Background: Tumor HER2 status remains a critical predictive biomarker for patients with breast cancer given the availability of highly effective anti-HER2 therapies. However, determination of HER2 status relies on a complex, tissue-dependent algorithm optimized for extremes of the phenotype – HER2+ vs HER2- status. Given the challenges in reliably obtaining informative tissue biopsies as disease progresses, the new therapeutic relevance of HER2-low breast cancer (IHC 1+ and FISH-negative IHC 2+), and the undependability of biomarkers in this range, better approaches are needed. Epigenomic signatures detectable in cell-free tumor DNA (cfDNA) may provide a minimally invasive, reliable determination of HER2 status. Here, we present a multimodal epigenomic liquid biopsy platform that could offer a minimally invasive alternative to tissue-based determination of transcriptionally regulated targets such as HER2. Methods: We identified patients with metastatic breast cancer (n=20) that were HER2-positive with IHC 3+ (n=6) or HER2-negative with IHC 0 (n=14) based on ASCO/CAP guidelines who underwent research biopsies at the Dana-Farber Cancer Institute from 2012-2023. Study participants had undergone concurrent blood sampling collected in Streck Tubes, processed as plasma, and stored at -80ºC. We applied a novel, multimodal epigenomic assay to profile tumor-derived gene regulatory programs from 1mL plasma. This genome-wide approach analyzed histone modifications associated with active gene promoters and enhancers, and DNA methylation. Classifier feature selection was derived from epigenomically profiled breast cancer cell lines. Final classifier performance was cross validated using a standard Leave one Out (LoO) scheme. Results: Our cross-validated classifier yielded an area under the ROC curve (AUC) of 0.91 in the 20 samples tested. When including only samples with detectable ctDNA as assessed by low pass whole genome sequencing (ichorCNA algorithm) (N=13), our classifier had an AUC of 0.98. Using regularized regression approach, we identified 33 loci with the highest predictive value; 8 loci were within the ERBB2 amplicon on chromosome 17, while the remaining 25 loci were in other genomic regions. We will present results from this and an expanded cohort, including participants with HER2 IHC 1+ and 2+ disease. Conclusions: We present a novel, minimally invasive HER2 classifier leveraging epigenomic information from cell-free tumor DNA from 1 mL plasma. If this approach remains robust in accurately classifying additional HER2 states, it could address the limitations of current tissue-based testing. Integration of this epigenomic platform in prospective interventional studies could support the development of novel and improved predictive classifiers for response to HER2-directed therapy. Citation Format: Heather Parsons, Sylvan Baca, Anthony D'Ippolito, Hyun-Hwan Jeong, Kalie Smith, Paolo Tarantino, Melissa Hughes, Kerry Sendrick, Corrie Painter, Ji-Heui Seo, Elizabeth Grant, Jamey Guess, Matthew Davidsohn, Gitanjali Lakshminarayanan, Sarah Strauss, Hunter Savignano, John Canniff, Brad Fortunato, Matthew Eaton, Toni Choueiri, Sara Tolaney, Matthew Freedman, Nancy Lin. Liquid biopsy determination of HER2 status in breast cancer: results from a novel epigenomic platform [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS06-07.