Independent Test Dataset Research Articles

Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I)

BackgroundTreatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models’ interpretability by ranking the features based on their predictive importance.MethodsWe included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC).ResultsEN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767–0.822) and AUC = 0.811 (95% CI 0.782–0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features.ConclusionsEN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future.

Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT.

Adaptive radiation treatment (ART) for locally advanced pancreatic cancer (LAPC) requires consistently accurate segmentation of the extremely mobile gastrointestinal (GI) organs at risk (OAR) including the stomach, duodenum, large and small bowel. Also, due to lack of sufficiently accurate and fast deformable image registration (DIR), accumulated dose to the GI OARs is currently only approximated, further limiting the ability to more precisely adapt treatments. Develop a 3-D Progressively refined joint Registration-Segmentation (ProRSeg) deep network to deformably align and segment treatment fraction magnetic resonance images (MRI)s, then evaluate segmentation accuracy, registration consistency, and feasibility for OAR doseaccumulation. ProRSeg was trained using five-fold cross-validation with 110 T2-weighted MRI acquired at five treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Statistical comparison to other deep learning and iterative registration methods were done using the Kruskal-Wallis test, followed by pair-wise comparisons with Bonferroni correction applied for multiple testing. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans was evaluated on a publicly available dataset of 80 scans using five-foldcross-validation. ProRSeg processed 3D volumes (128 × 192 × 128) in 3 s on a NVIDIA Tesla V100 GPU. It's segmentations were significantly more accurate ( ) than compared methods, achieving a DSC of 0.94 ±0.02 for liver, 0.88±0.04 for large bowel, 0.78±0.03 for small bowel and 0.82±0.04 for stomach-duodenum from MRI. ProRSeg achieved a DSC of 0.72±0.01 for small bowel and 0.76±0.03 for stomach-duodenum from public CBCT dataset. ProRSeg registrations resulted in the lowest CV in displacement (stomach-duodenum : 0.75%, : 0.73%, and : 0.81%; small bowel : 0.80%, : 0.80%, and : 0.68%; large bowel : 0.71%, : 0.81%, and : 0.75%). ProRSeg based dose accumulation accounting for intra-fraction (pre-treatment to post-treatment MRI scan) and inter-fraction motion showed that the organ dose constraints were violated in four patients for stomach-duodenum and for three patients for small bowel. Study limitations include lack of independent testing and ground truth phantom datasets to measure dose accumulationaccuracy. ProRSeg produced more accurate and consistent GI OARs segmentation and DIR of MRI and CBCTs compared to multiple methods. Preliminary results indicates feasibility for OAR dose accumulation usingProRSeg.

A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity.

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death. The authors developed an innovative framework consisting of ResNet-18, a convolutional neural network architecture, with the objective of quantitatively mapping a complete blood count (CBC) scattergram to quickly and robustly indicate a probable susceptibility to APL. Three hundred and twenty scattergrams of the white blood cell differential channel from 51 patients with APL, 510 scattergrams from 105 patients who had non-APL AML, and 320 scattergrams from 320 healthy controls were randomly stratified at a ratio of 4:1 and split into training and testing data sets to accomplish five-fold cross-validation. Both the area under the curve and the average precision of >0.99 were achieved in each fold. Three hundred four of the 320 APL scattergrams (95%) were correctly flagged by the model, which outcompeted the CBC review rules recommended by the International Society of Laboratory Hematology (all p<.001). External validation based on an independent testing data set that included 56 scattergrams from 31 patients with APL, 56 scattergrams from 55 patients with non-APL AML, and 64 scattergrams from 64 healthy controls also confirmed the sensitivity and specificity of the framework. To the authors' knowledge, their convolutional neural network-based framework is the first to use scattergram output from routine CBC analysis to map suspicious APL early with outstanding sensitivity, specificity, and precision. The authors also describe a new CBC workflow incorporating this framework upstream of the morphologic review, which would provide the earliest flag for APL. The authors propose an innovative way to visualize complete blood counts (CBCs) by mapping the difference in white blood cell counts using automated CBC analysis to identify potential acute promyelocytic leukemia (APL) using a convolutional neural network (CNN), which can eliminate the potential pitfalls of manual observation. Analyses of an unprecedented, realistic data set validated that the quantitative relationship between the CBC scattergram and an APL abnormality is highly consistent. This is the first study to date focusing on screening for APL using scattergrams of the difference in white blood cell counts from routine CBC tests and has significant clinical relevance. The authors recommend using this method even before analyzing cell images, which could provide the earliest way to screen for APL in a sensitive and accurateway.

A contingency approach for time-cost trade-off in construction projects based on machine learning techniques

PurposeTime-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.Design/methodology/approachA preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).FindingsThe model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.Originality/valueThe identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.

IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity.

CD8+ T cells can recognize peptides presented by class I human leukocyte antigen (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, the wealth of data generated by experiments has spawned many computational approaches for predicting HLA-I binding, antigen presentation and T-cell immune responses. Nevertheless, existing HLA-I binding and antigen presentation prediction approaches suffer from low precision due to the absence of T-cell receptor (TCR) recognition. Direct modeling of T-cell immune responses is less effective as TCR recognition's mechanism still remains underexplored. Therefore, directly applying these existing methods to screen cancer neoantigens is still challenging. Here, we propose a novel immune epitope prediction method termed IEPAPI by effectively incorporating antigen presentation and immunogenicity. First, IEPAPI employs a transformer-based feature extraction block to acquire representations of peptides and HLA-I proteins. Second, IEPAPI integrates the prediction of antigen presentation prediction into the input of immunogenicity prediction branch to simulate the connection between the biological processes in the T-cell immune response. Quantitative comparison results on an independent antigen presentation test dataset exhibit that IEPAPI outperformed the current state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25/25) and 76% (19/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the best precision on two independent neoantigen datasets when compared with existing approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine design.

AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information

Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5–100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%–6.13%), MCC (2.92%–12.86%) and AUC (5.13%–8.56%) and AP (9.20%–21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R2 on a public dataset by 10-fold cross-validation with an improvement of (18.82%–19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.

BrcaDx: precise identification of breast cancer from expression data using a minimal set of features.

Background: Breast cancer is the foremost cancer in worldwide incidence, surpassing lung cancer notwithstanding the gender bias. One in four cancer cases among women are attributable to cancers of the breast, which are also the leading cause of death in women. Reliable options for early detection of breast cancer are needed. Methods: Using public-domain datasets, we screened transcriptomic profiles of breast cancer samples, and identified progression-significant linear and ordinal model genes using stage-informed models. We then applied a sequence of machine learning techniques, namely, feature selection, principal components analysis, and k-means clustering, to train a learner to discriminate "cancer" from "normal" based on expression levels of identified biomarkers. Results: Our computational pipeline yielded an optimal set of nine biomarker features for training the learner, namely, NEK2, PKMYT1, MMP11, CPA1, COL10A1, HSD17B13, CA4, MYOC, and LYVE1. Validation of the learned model on an independent test dataset yielded a performance of 99.5% accuracy. Blind validation on an out-of-domain external dataset yielded a balanced accuracy of 95.5%, demonstrating that the model has effectively reduced the dimensionality of the problem, and learnt the solution. The model was rebuilt using the full dataset, and then deployed as a web app for non-profit purposes at: https://apalania.shinyapps.io/brcadx/. To our knowledge, this is the best-performing freely available tool for the high-confidence diagnosis of breast cancer, and represents a promising aid to medical diagnosis.

The effect of short-term training about depth predicting score on the diagnostic ability of invasion depth for differentiated early gastric Cancer among non-expert endoscopists

BackgroundThe depth-predicting score (DPS) was proposed based on conventional white-light imaging (C-WLI) endoscopic features of early gastric cancer (EGC) to determine the invasion depth of the neoplasm. However, the effect of DPS on training endoscopists remains unclear. Therefore, we aimed to investigate the effect of short-term DPS training on improving the diagnostic ability of EGC invasion depth and compare the training effect among non-expert endoscopists at different levels.MethodsIn the training session, the definitions and scoring rules of DPS were instructed, and classic C-WLI endoscopic example graphics were exhibited to the participants. Another C-WLI endoscopic images of 88 cases of histologically proven differentiated EGC were selected as an independent test dataset for evaluating the training effect. Each participant was tested, and the diagnostic accuracy rate of invasion depth was calculated differently one week before the training and after the completion of training.ResultsA total of 16 participants were enrolled and completed the training. Participants were divided into a trainee group and a junior endoscopist group according to the total number of C-WLI endoscopies performed. The total number of C-WLI endoscopies performed showed a significant difference between the trainee group and junior endoscopist group (350 vs. 2500, P = 0.001). No significant difference between the trainee group and junior endoscopist group was observed for pre-training accuracy. The overall diagnostic accuracy of invasion depth was improved significantly after completing DPS training compared with before (68.75 ± 5.71% vs. 61.58 ± 9.61%, P = 0.009). In the subgroup analysis, the post-training accuracy was higher than the pre-training accuracy, but significant improvement was observed only in the trainee group (61.65 ± 7.33% vs. 68.32 ± 5.71%, P = 0.034). In addition, no significant difference in post-training accuracy between the two groups was observed.ConclusionShort-term DPS training can improve the diagnostic ability of the invasion depth of EGC and homogenize the diagnostic ability of non-expert endoscopists at different levels. The depth-predicting score was convenient and effective for endoscopist training.

EmPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model.

Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein-DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.

EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species

Methylation is a major DNA epigenetic modification for regulating the biological processes without altering the DNA sequence, and multiple types of DNA methylations have been discovered, including 6mA, 5hmC, and 4mC. Multiple computational approaches were developed to automatically identify the DNA methylation residues using machine learning or deep learning algorithms. The machine learning (ML) based methods are difficult to be transferred to the other predicting tasks of the DNA methylation sites using additional knowledge. Deep learning (DL) may facilitate the transfer learning of knowledge from similar tasks, but they are often ineffective on small datasets. This study proposes an integrated feature representation framework EpiTEAmDNA based on the strategies of transfer learning and ensemble learning, which is evaluated on multiple DNA methylation types across 15 species. EpiTEAmDNA integrates convolutional neural network (CNN) and conventional machine learning methods, and shows improved performances than the existing DL-based methods on small datasets when no additional knowledge is available. The experimental data suggests that the EpiTEAmDNA models may be further improved via transfer learning based on additional knowledge. The evaluation experiments on the independent test datasets also suggest that the proposed EpiTEAmDNA framework outperforms the existing models in most prediction tasks of the 3 DNA methylation types across 15 species. The source code, pre-trained global model, and the EpiTEAmDNA feature representation framework are freely available at http://www.healthinformaticslab.org/supp/.

O-GlyThr: Prediction of human O-linked threonine glycosites using multi-feature fusion

O-linked glycosylation is one of the most complex post-translational modifications (PTM) of human proteins modulating various cellular metabolic and signaling pathways. Unlike N-glycosylation, the O-glycosylation has non-specific sequence features and unstable glycan core structure, which makes identification of O-glycosites more challenging either by experimental or computational methods. Biochemical experiments to identify O-glycosites in batches are technically and economically demanding. Therefore, development of computation-based methods is greatly warranted. This study constructed a prediction model based on feature fusion for O-glycosites linked to the threonine residues in Homo sapiens. In the training model, we collected and sorted out high-quality human protein data with O-linked threonine glycosites. Seven feature coding methods were fused to represent the sample sequence. By comparison of different algorithms, random forest was selected as the final classifier to construct the classification model. Through 5-fold cross-validation, the proposed model, namely O-GlyThr, performed satisfactorily on both training set (AUC: 0.9308) and independent validation dataset (AUC: 0.9323). Compared with previously published predictors, O-GlyThr achieved the highest ACC of 0.8475 on the independent test dataset. These results demonstrated the high competency of our predictor in identifying O-glycosites on threonine residues. Furthermore, a user-friendly webserver named O-GlyThr (http://cbcb.cdutcm.edu.cn/O-GlyThr/) was developed to assist glycobiologists in the research associated with glycosylation structure and function.

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds.

Towards robustness and sensitivity of rapid Baijiu (Chinese liquor) discrimination using Raman spectroscopy and chemometrics: Dimension reduction, machine learning, and auxiliary sample

Rapid and non-invasive discrimination of two types of similar baijiu with the same alcohol content was investigated using chemometrics-assisted Raman spectroscopy. Baijiu samples manufactured across six months were subjected to Raman spectrum acquisition to form chronologically independent training and testing data sets for the optimization and evaluation of chemometric approaches. Discrimination capacities of machine learning (ML) algorithms were enhanced by the supervised spectral dimension reduction with linear discrimination analysis (LDA). The LDA-LightGBM model optimized with the binary training set led to an overall testing discrimination accuracy of 79%. Supplementing the Raman spectra of an auxiliary type of baijiu into the binary training data set considerably enhances the feature extraction capacity of LDA. Performances of the LDA-ML models were further elevated by the spectral preprocessing of “constant background subtraction” that shifts each spectrum downwards by its spectral intensity minimum. The chemometric approach that involved auxiliary training data, constant background subtraction, LDA dimension reduction, and ensemble learning classifier contributed to an overall testing discrimination accuracy of 97%. The proposed chemometric-assisted Raman spectroscopic approach facilitated robust and sensitive discrimination of highly similar baijiu products, which highlights the potential applications of the technique in manufacturing quality control and product authentication.

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

EdeepSADPr: an extensive deep-learning architecture for prediction of the in situ crosstalks of serine phosphorylation and ADP-ribosylation.

The in situ post-translational modification (PTM) crosstalk refers to the interactions between different types of PTMs that occur on the same residue site of a protein. The crosstalk sites generally have different characteristics from those with the single PTM type. Studies targeting the latter's features have been widely conducted, while studies on the former's characteristics are rare. For example, the characteristics of serine phosphorylation (pS) and serine ADP-ribosylation (SADPr) have been investigated, whereas those of their in situ crosstalks (pSADPr) are unknown. In this study, we collected 3,250 human pSADPr, 7,520 SADPr, 151,227pS and 80,096 unmodified serine sites and explored the features of the pSADPr sites. We found that the characteristics of pSADPr sites are more similar to those of SADPr compared to pS or unmodified serine sites. Moreover, the crosstalk sites are likely to be phosphorylated by some kinase families (e.g., AGC, CAMK, STE and TKL) rather than others (e.g., CK1 and CMGC). Additionally, we constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. We also used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. The best classifiers had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites, respectively. The lowest prediction accuracy was achieved by separating pSADPr and SADPr sites, which is consistent with the observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/. We expect our investigation will promote a comprehensive understanding of crosstalks.

Evaluating semi- and nonparametric regression algorithms in quantifying stem taper and volume with alternative test data selection strategies

Abstract Accurately quantifying stem taper is essential to predict diameter at any given height along the stem and to estimate tree volume for various sections of the stem. With increased computing power, semi- and nonparamatric methods have been proposed as alternative approaches for modelling tree taper. The main objective of this study was to assess the accuracy of stem taper predicted for four pine and four hardwood species by semi- and nonparametric models. Specifically, generalized additive models (GAM), random forests (RF) and regression-enhanced random forests (RERF) were compared with two widely-used parametric models: variable-exponent function (VAR) and segmented polynomial regression model (SEG). Test datasets selected from four different data splitting methods were used to examine the prediction accuracy of the models. Results showed that all examined models can be used to quantify stem taper and volume for all species when prediction is limited to be within the range of tree sizes used in model building. The nonparametric RF algorithm generally produced higher bias and lower precision than the semiparametric (GAM and RERF) and parametric models (VAR and SEG). For all species, GAM, VAR and SEG provided more robust predictions of stem taper than RF and RERF algorithms, especially when small or large trees were withheld for model testing. The data splitting strategies used in this work provide an efficient ‘stress test’ to evaluate model performance when collecting an independent test dataset is not feasible. The findings of this work provide additional insights for forest practitioners and resource managers to select appropriate methods in stem taper modelling.

An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning.

Immunofluorescence patterns of anti-nuclear antibodies (ANAs) on human epithelial cell (HEp-2) substrates are important biomarkers for the diagnosis of autoimmune diseases. There are growing clinical requirements for an automatic readout and classification of ANA immunofluorescence patterns for HEp-2 images following the taxonomy recommended by the International Consensus on Antinuclear Antibody Patterns (ICAP). In this study, a comprehensive collection of HEp-2 specimen images covering a broad range of ANA patterns was established and manually annotated by experienced laboratory experts. By utilizing a supervised learning methodology, an automatic immunofluorescence pattern classification framework for HEp-2 specimen images was developed. The framework consists of a module for HEp-2 cell detection and cell-level feature extraction, followed by an image-level classifier that is capable of recognizing all 14 classes of ANA immunofluorescence patterns as recommended by ICAP. Performance analysis indicated an accuracy of 92.05% on the validation dataset and 87% on an independent test dataset, which has surpassed the performance of human examiners on the same test dataset. The proposed framework is expected to contribute to the automatic ANA pattern recognition in clinical laboratories to facilitate efficient and precise diagnosis of autoimmune diseases.

Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification.

Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully.

Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Abstract. The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO2 (pCO2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface pCO2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface pCO2 in the SCS with a reasonably high spatial resolution (0.05∘ × 0.05∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of pCO2 constrained by EOF, atmospheric pCO2, and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed pCO2 field successfully simulated the spatial and temporal patterns of sea surface pCO2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm, which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed pCO2 and in situ data ranged from −10 to 4 µatm (−2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface pCO2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface pCO2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface pCO2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO2, the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at https://doi.org/10.57760/sciencedb.02050 (Wang and Dai, 2022).

Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture

BackgroundIdentifying thyroid nodules’ boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands.MethodsThe 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes.ResultsDSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net.ConclusionsOur method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.