Bootstrapping Strategy Research Articles

Advancing Algorithmic Adaptability in Hyperspectral Anomaly Detection with Stacking-Based Ensemble Learning

Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key source of this dichotomy may center on the singular and like biases frequently employed by existing algorithms. Current research lacks experimentation into how integrating insights from diverse biases might counteract problems in singularly biased approaches. Addressing this gap, we propose stacking-based ensemble learning for hyperspectral anomaly detection (SELHAD). SELHAD introduces the integration of hyperspectral anomaly detection algorithms with diverse biases (e.g., Gaussian, density, partition) into a singular ensemble learning model and learns the factor to which each bias should contribute so anomaly detection performance is optimized. Additionally, it introduces bootstrapping strategies into hyperspectral anomaly detection algorithms to further increase robustness. We focused on five representative algorithms embodying common biases in hyperspectral anomaly detection and demonstrated how they result in the previously highlighted dichotomy. Subsequently, we demonstrated how SELHAD learns the interplay between these biases, enabling their collaborative utilization. In doing so, SELHAD transcends the limitations inherent in individual biases, thereby alleviating the dichotomy and advancing toward more adaptable solutions.

Pathways to Flourishing: The Roles of Self- and Divine Forgiveness in Mitigating the Adverse Effects of Stress and Substance Use among Adults in Trinidad and Tobago

The present study focused on the roles of self-forgiveness and feeling divine forgiveness in mitigating the adverse effects of stress on substance use cravings in Trinidad and Tobago. We assessed 869 individuals (62 percent of whom were women) through self-report online questionnaires. A moderation analysis using the PROCESS macro and a bootstrapping strategy (N = 5000) revealed that greater levels of self-forgiveness weakened the positive relationship between stress and substance use cravings, though the relationship remained significant. This moderating effect was evident only when individuals reported high levels of feeling divine forgiveness. The findings suggest that both forms of forgiveness lessen the impact of stress on substance use cravings, highlighting their potential as protective factors and underscoring the importance of incorporating religious and spiritual dimensions into psychological education and intervention. However, the study’s cross-sectional nature makes it difficult to make causal inferences, indicating a need for longitudinal research.

Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model.

An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches

Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes.

WHRIME: A weight-based recursive hierarchical RIME optimizer for breast cancer histopathology image segmentation

In medical image processing, multi-threshold image segmentation has been challenging, as selecting appropriate thresholds is crucial for distinguishing different structures within an image, especially when dealing with breast cancer images. Breast cancer images are complex with multiple tissue types, which pose challenges to precise diagnosis. A weight-based recursive hierarchical bootstrapping rime algorithm (WHRIME) is proposed to tackle this case effectively. The proposed WHRIME segregates the population into elite and non-elite individuals. The hierarchical bootstrapping strategy ensures comprehensive exploration of the solution space, with elite individuals guiding the position updates of non-elite individuals. A weighted method is introduced based on solution quality differences to maintain population diversity and enhance convergence accuracy. We apply WHRIME in multi-threshold image segmentation on breast cancer histopathology images. Experimental results on both the IEEE CEC 2017 benchmark suit and breast cancer histopathology images from the Databiox dataset validate the superiority of WHRIME over competing algorithms, affirming WHRIME’s capability in addressing complex breast cancer histopathology image segmentation problems.

BIBLIOMETRIC ANALYSIS AND VISUALIZATION OF BOOTSTRAPPING STRATEGY ARTICLES BY INDONESIAN AUTHORS

This research aims to analyze bibliometric characteristics and trends of crowdfunding bootstrapping strategy articles indexed by Indonesian authors. Information was extracted from the Google Scholar database. The study employed bootstrapping and its variations as search terms, with Indonesian author affiliations considered. Simple statistical techniques were applied, and VOS Viewer software was utilized for bibliometric analysis. In this study, patterns of keyword co-occurrence, document citations, citation relationships, and bibliographic coupling were visualized. The results indicate that the main trend in current startup research is the adoption of bootstrapping strategy, particularly network-based bootstrapping, due to its ability to support startup growth without relying on external funding but rather on a network.

Factors Affecting Funding Choices of Entrepreneurs in India: An Analytical Study

Funding Choices of Entrepreneurs in India encompass a multitude of variables that shape the decisions made by entrepreneurs in securing financial resources for their ventures. These factors include the availability and accessibility of formal financing options, such as loans and investment capital, as well as the challenges related to collateral requirements and the stringent criteria imposed by financial institutions. Limited awareness and understanding of financing alternatives among entrepreneurs also contribute to their funding choices. Moreover, the competitive landscape and market conditions exert a profound influence on funding decisions, with the presence of unanticipated competition, market volatility, and the need for rapid growth and innovation leading entrepreneurs to seek funding from unconventional sources or adopt bootstrapping strategies. Understanding these factors is crucial for entrepreneurs, policymakers, and financial institutions to develop initiatives that enhance the accessibility of formal financing, promote financial literacy and entrepreneurial education, and foster a supportive ecosystem for entrepreneurs to thrive. Financial institutions can tailor their offerings to meet the unique needs of entrepreneurs, while policymakers can implement measures to create an enabling environment for entrepreneurial finance. By comprehending these factors, stakeholders can effectively address the funding challenges faced by entrepreneurs in India and facilitate their entrepreneurial endeavors for sustainable economic growth and innovation.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissuesalgorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

Calibration transfer by likelihood maximization: A standard-free approach capable of handling non-overlapping wavelength ranges

Calibration transfer is useful when modeling data collected from different, yet similar, sources. This may occur when using spectrophotometers to monitor an industrial process. The objective of this method is to compensate for differences between the instruments and reduce any biases that may arise. Calibration transfer seeks to apply a multivariate model to a validation dataset that differs in some meaningful way from the calibration set. However, making robust calibration transfer models typically requires the user to acquire many observations in both datasets and ensure that some observations are present in both the calibration and validation datasets. Our novel methodology seeks to address these issues, which are often cumbersome in an industrial context. The proposed methodology is not a typical calibration transfer method but has the same role as it estimates the observations acquired from one X2 domain into another X1 domain. We implement a bootstrapping strategy to quantify the estimates of the spectra in the new domain and the uncertainty associated with them. These results are then combined with an inverse multivariate regression to predict the y values, which is known as the likelihood maximization inverse regression (LMIR) methodology. In this paper, we first present the algorithms used for estimation and prediction. The proposed method is tested on two real datasets and the results are compared with those of three other methods that are typically used to perform direct calibration, namely, partial least squares (PLS), powered PLS and direct LMIR. A comparison to calibration methods is made here to provide a reference for the results obtained and demonstrate the benefits of using our estimation model. These results reveal that the proposed methodology outperforms the standard predictive methods. Further, it is beneficial because it does not need standard samples and is capable of handling different wavelength ranges and variables.

AEP: Aligning knowledge graphs via embedding propagation

Knowledge graph alignment aims to identify entity pairs having the same meaning between different knowledge graphs, which is essential to the automated construction of a coherent knowledge base. With the development of knowledge representation learning, researchers have proposed several useful embedding-based alignment methods. In practice, some entities are easily aligned with high confidence while others are not. However, there is a lack of effective methods which boost the alignment of implicitly aligned entities based on explicitly aligned ones. This paper presents AEP, which combines several effective schemes for accurate knowledge graph alignment. First, we employ a word-embedding model to encode the semantic information contained in entities’ surface names. We propose an attention-based graph convolutional network model to incorporate the structural information via supervised embedding propagation. Besides, we develop a multi-view bootstrapping strategy to address the over-fitting problem caused by insufficient training sets. Next, an embedding-propagation-based alignment scheme is proposed to improve the alignment accuracy by propagating explicitly aligned entities’ embeddings to implicitly aligned ones in an unsupervised manner. Finally, we conduct extensive experiments to validate the superiority of AEP and evaluate the effects of proposed schemes. Experimental results show that AEP outperforms state-of-the-art methods, and the proposed schemes improve the alignment accuracy significantly.

Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer

Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture.

Bootstrapping a Two-Loop Four-Point Form Factor.

We compute the two-loop four-point form factor of a length-3 half-BPS operator in planar N=4 SYM theory, which belongs to the class of two-loop five-point scattering observables with one off-shell color-singlet leg. A new bootstrapping strategy is developed to obtain this result by starting with an ansatz expanded in terms of master integrals and then solving the master coefficients via various physical constraints. We find that consistency conditions of infrared divergences and collinear limits, together with the cancellation of spurious poles, can fix a significant part of the ansatz. The remaining degrees of freedom can be fixed by one simple type of two-double unitarity cut. Full analytic results in terms of both symbol and Goncharov polylogarithms are provided.

Data set entity recognition based on distant supervision

PurposeThis paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.Design/methodology/approachFirstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.FindingsIn the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.Originality/valueThis paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

Spatial RNA Sequencing Identifies Robust Markers of Vulnerable and Resistant Human Midbrain Dopamine Neurons and Their Expression in Parkinson's Disease.

Defining transcriptional profiles of substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) dopamine neurons is critical to understanding their differential vulnerability in Parkinson’s Disease (PD). Here, we determine transcriptomes of human SNc and VTA dopamine neurons using LCM-seq on a large sample cohort. We apply a bootstrapping strategy as sample input to DESeq2 and identify 33 stably differentially expressed genes (DEGs) between these two subpopulations. We also compute a minimal sample size for identification of stable DEGs, which highlights why previous reported profiles from small sample sizes display extensive variability. Network analysis reveal gene interactions unique to each subpopulation and highlight differences in regulation of mitochondrial stability, apoptosis, neuronal survival, cytoskeleton regulation, extracellular matrix modulation as well as synapse integrity, which could explain the relative resilience of VTA dopamine neurons. Analysis of PD tissues showed that while identified stable DEGs can distinguish the subpopulations also in disease, the SNc markers SLIT1 and ATP2A3 were down-regulated and thus appears to be biomarkers of disease. In summary, our study identifies human SNc and VTA marker profiles, which will be instrumental for studies aiming to modulate dopamine neuron resilience and to validate cell identity of stem cell-derived dopamine neurons.

A Progressive Approach for Uncertainty Visualization in Diffusion Tensor Imaging

AbstractDiffusion Tensor Imaging (DTI) is a non‐invasive magnetic resonance imaging technique that, combined with fiber tracking algorithms, allows the characterization and visualization of white matter structures in the brain. The resulting fiber tracts are used, for example, in tumor surgery to evaluate the potential brain functional damage due to tumor resection. The DTI processing pipeline from image acquisition to the final visualization is rather complex generating undesirable uncertainties in the final results. Most DTI visualization techniques do not provide any information regarding the presence of uncertainty. When planning surgery, a fixed safety margin around the fiber tracts is often used; however, it cannot capture local variability and distribution of the uncertainty, thereby limiting the informed decision‐making process. Stochastic techniques are a possibility to estimate uncertainty for the DTI pipeline. However, it has high computational and memory requirements that make it infeasible in a clinical setting. The delay in the visualization of the results adds hindrance to the workflow. We propose a progressive approach that relies on a combination of wild‐bootstrapping and fiber tracking to be used within the progressive visual analytics paradigm. We present a local bootstrapping strategy, which reduces the computational and memory costs, and provides fiber‐tracking results in a progressive manner. We have also implemented a progressive aggregation technique that computes the distances in the fiber ensemble during progressive bootstrap computations. We present experiments with different scenarios to highlight the benefits of using our progressive visual analytic pipeline in a clinical workflow along with a use case and analysis obtained by discussions with our collaborators.

Ensemble Spatial Interpolation: A New Approach to Natural or Anthropogenic Variable Assessment

This paper presents a novel and versatile framework for building ensemble spatial interpolation functions. As with all ensemble methods, the central idea is to assemble a voting scheme where a set of weak interpolation functions are combined, by using an aggregation function, to produce a strong ensemble response. In the presented scheme, voter interpolation functions are weak because they deal with a minimal portion of sample data extracted from spatial random partition elements, while the ensemble as a whole uses all the available information as much as possible. The random partitions scheme behaves as a bootstrapping strategy applied in a spatial context. Experiments show that the proposed framework has the promising ability to produce robust interpolation functions that can both scale to handle large sample data sets and deal with uncertainty quantifications, although weak voter interpolation functions are deterministic or highly data-consuming.

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype

Genotype plays a significant role in determining characteristics in an organism and genotype calling has been greatly accelerated by sequencing technologies. Furthermore, most parametric statistical models are unable to effectively call genotype, which is influenced by the size of structural variations and the coverage fluctuations of sequencing data. In this study, we propose a new method for calling deletions’ genotypes from the next-generation data, called Cnngeno. Cnngeno can convert sequencing data into images and classifies the genotypes from these images using the convolutional neural network(CNN). Moreover, Cnngeno adopted the convolutional bootstrapping strategy to improve the anti-noisy label’s ability. The results show that Cnngeno performs better in terms of precision for calling genotype when compared with other existing methods. The Cnngeno is an open-source method, available at https://github.com/BRF123/Cnngeno.

Additive trees for the categorization of a large number of objects, with bootstrapping strategy for stability assessment. Application to the free sorting of wine odor terms

In the field of clustering techniques, little attention has been paid to the recovery of a set of clusters from the structure of an additive tree. To bridge this gap, this work presents an original partitioning technique which aims to reveal clusters from an additive tree that represents a large set of objects. Specifically, an algorithm that splits a tree into successive subtrees was developed, based on a ratio of the lengths of edges. The stability of the clusters obtained with this technique was then evaluated using measurements of cohesion and isolation that were generated using a bootstrapping strategy. Finally, the degree of association of each object to clusters was analyzed to gain insight into their internal structure. This analysis was performed on the results of a sorting task conducted by 156 subjects, who were asked to sort 96 terms associated with the odor of wine. The methodology developed in this paper represents an innovative way to highlight groups of terms within a large set of wine odor attributes, with the ultimate goal being to improve the structure of the lexicon.

The impact of improvisation and financial bootstrapping strategies on business performance

PurposeThis paper aims to examine the effect of financial bootstrapping strategies (FBS) and strategic improvisation (SI) on business performance (BP). The study enriches our understanding of the contributions of bootstrapping and improvisation strategies toward resource-constrained small businesses during real economic downturns and crises. The potential moderating effect of SI on the relationship between FBS and its dimensions and performance were also examined.Design/methodology/approachUsing the convenience snowball sampling technique, data were collected from entrepreneurs in Tripoli, Libya. Structural equation modeling by means of partial least square bootstrapping resampling was used for the hypotheses testing of the 147 useable responses.FindingsStatistically significant positive relationships were found in the direct relationships between bootstrapping and improvisation with performance. However, there was no significant association found between the delaying payment related bootstrapping and the owner-related bootstrapping with performance. The moderating effect of improvisation had a significant relationship between bootstrapping as an aggregate construct and its dimensions and performance.Research limitations/implicationsDue to the cross-sectional nature of this study which used a small sample that was randomly selected, generalization to the entire population of business ventures should be made with caution.Practical implicationsThe negative moderation effect of improvisation on FBS-BP association suggests that entrepreneurs need to be careful in balancing the two strategies so that efforts are no wasted.Originality/valueWhile business performance has been studied in various organizations, its examination with financial bootstrapping strategies as a predictor and strategic improvisation as a moderator contribute nascent theoretical insights.

Comprehensive Network Analysis Reveals Alternative Splicing-Related lncRNAs in Hepatocellular Carcinoma.

It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs.