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Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication. 评估可解释多模态深度学习中用于癌症预测的新兴预训练策略。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-07-22 DOI: 10.1186/s13040-023-00338-w
Zarif L Azher, Anish Suvarna, Ji-Qing Chen, Ze Zhang, Brock C Christensen, Lucas A Salas, Louis J Vaickus, Joshua J Levy

Background: Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication, further illustrating the potential utility of such methods. However, current approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to "pretrain" models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. In addition, model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and trust in the technology.

Methods: Here, we develop an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology (i.e., tissue slides) data, and we compare performance of crossmodal pretraining, contrastive learning, and transfer learning versus the standard procedure.

Results: Our models outperform the existing state-of-the-art method (average 11.54% C-index increase), and baseline clinically driven models (average 11.7% C-index increase). Model interpretations elucidate consideration of biologically meaningful factors in making prognosis predictions.

Discussion: Our results demonstrate that the selection of pretraining strategies is crucial for obtaining highly accurate prognostication models, even more so than devising an innovative model architecture, and further emphasize the all-important role of the tumor microenvironment on disease progression.

背景:深度学习模型可以从分子和解剖病理信息中推断癌症患者的预后。最近的研究利用了来自互补多模态数据的信息,改善了预测,进一步说明了这些方法的潜在效用。然而,目前的方法:1)没有全面利用生物和组织形态学的关系,2)利用新兴的策略来“预训练”模型(即,在稍微正交的数据集/建模目标上训练模型),这可能通过减少实现最佳性能所需的信息量来帮助预测。此外,通过培养从业者对技术的理解和信任,模型解释对于促进临床采用深度学习方法至关重要。方法:在这里,我们开发了一个可解释的多模态建模框架,该框架结合了DNA甲基化、基因表达和组织病理学(即组织切片)数据,并将跨模态预训练、对比学习和迁移学习的性能与标准程序进行了比较。结果:我们的模型优于现有的最先进的方法(平均11.54%的c -指数增加)和基线临床驱动模型(平均11.7%的c -指数增加)。模型解释阐明了在进行预后预测时考虑生物学上有意义的因素。讨论:我们的研究结果表明,选择预训练策略对于获得高度准确的预测模型至关重要,甚至比设计创新的模型架构更重要,并进一步强调了肿瘤微环境在疾病进展中的重要作用。
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引用次数: 2
Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study. 基于神经网络的胃癌预后预测工具:全球回顾性研究。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-07-18 DOI: 10.1186/s13040-023-00335-z
Wei Li, Minghang Zhang, Siyu Cai, Liangliang Wu, Chao Li, Yuqi He, Guibin Yang, Jinghui Wang, Yuanming Pan

Backgrounds: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients.

Methods: In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance.

Results: The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428).

Conclusions: GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

背景:胃贲门癌(GCC)近年来发病率明显上升,预后较差。有必要将GCC与其他胃部位癌的预后进行比较,建立有效的基于神经网络的预后模型来预测GCC患者的生存。方法:在基于人群的队列研究中,我们首先纳入了来自监测、流行病学和最终结果(SEER)数据(n = 31,397)以及来自不同医院的中国公开数据(n = 1049)的临床特征。然后根据诊断时间将SEER数据分为两组,训练组(2010-2014年诊断为GCC的患者,n = 4414)和测试组(2015年诊断为GCC的患者,n = 957)。选择年龄、性别、病理、肿瘤、淋巴结和转移(TNM)分期、肿瘤大小、是否手术、是否放疗、是否化疗和恶性肿瘤史作为预测临床特征。利用列车队列进行基于神经网络的预后预测模型,并通过自身和测试队列的验证。采用受试者工作特性曲线下面积(AUC)评价模型性能。结果:SEER数据库中GCC患者的预后差于非GCC (NGCC)患者,而在中国数据中并不差。模型共纳入5371例患者,遵循纳入和排除标准。基于神经网络的预后预测模型对GCC总生存期(OS)的预测效果令人满意,在训练队列中AUC为0.7431(95%置信区间CI为0.7423-0.7439),在测试队列中AUC为0.7419 (95% CI为0.7411-0.7428)。结论:与非GCC患者相比,GCC患者的生存时间确实存在差异。本研究开发的基于神经网络的预后预测工具是一种新颖而有前途的用于GCC患者临床结果分析的软件。
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引用次数: 0
Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks. 基于集成神经网络的改进SIRD流行病模型参数辨识逆问题。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-07-18 DOI: 10.1186/s13040-023-00337-x
Marian Petrica, Ionel Popescu

In this paper, we propose a parameter identification methodology of the SIRD model, an extension of the classical SIR model, that considers the deceased as a separate category. In addition, our model includes one parameter which is the ratio between the real total number of infected and the number of infected that were documented in the official statistics. Due to many factors, like governmental decisions, several variants circulating, opening and closing of schools, the typical assumption that the parameters of the model stay constant for long periods of time is not realistic. Thus our objective is to create a method which works for short periods of time. In this scope, we approach the estimation relying on the previous 7 days of data and then use the identified parameters to make predictions. To perform the estimation of the parameters we propose the average of an ensemble of neural networks. Each neural network is constructed based on a database built by solving the SIRD for 7 days, with random parameters. In this way, the networks learn the parameters from the solution of the SIRD model. Lastly we use the ensemble to get estimates of the parameters from the real data of Covid19 in Romania and then we illustrate the predictions for different periods of time, from 10 up to 45 days, for the number of deaths. The main goal was to apply this approach on the analysis of COVID-19 evolution in Romania, but this was also exemplified on other countries like Hungary, Czech Republic and Poland with similar results. The results are backed by a theorem which guarantees that we can recover the parameters of the model from the reported data. We believe this methodology can be used as a general tool for dealing with short term predictions of infectious diseases or in other compartmental models.

在本文中,我们提出了SIRD模型的参数识别方法,这是经典SIR模型的扩展,将死者视为一个单独的类别。此外,我们的模型还包括一个参数,即实际感染总人数与官方统计中记录的感染人数之间的比率。由于政府决策、多种变量的流通、学校的开办和关闭等因素的影响,模型参数长时间保持不变的典型假设是不现实的。因此,我们的目标是创建一种短时间内有效的方法。在这个范围内,我们依靠前7天的数据接近估计,然后使用识别的参数进行预测。为了对参数进行估计,我们提出了神经网络集合的平均值。每个神经网络都是基于求解7天的SIRD所建立的数据库来构建的,具有随机参数。通过这种方式,网络从SIRD模型的解中学习参数。最后,我们使用集合从罗马尼亚covid - 19的实际数据中获得参数的估计,然后我们说明了不同时期(从10天到45天)对死亡人数的预测。主要目标是将这种方法应用于分析罗马尼亚的COVID-19演变,但匈牙利、捷克共和国和波兰等其他国家也采用了这种方法,取得了类似的结果。结果得到了一个定理的支持,该定理保证了我们可以从报告的数据中恢复模型的参数。我们认为,这种方法可以作为处理传染病短期预测的通用工具或用于其他隔间模型。
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引用次数: 0
ChatGPT and large language models in academia: opportunities and challenges. 学术界的 ChatGPT 和大型语言模型:机遇与挑战。
IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-07-13 DOI: 10.1186/s13040-023-00339-9
Jesse G Meyer, Ryan J Urbanowicz, Patrick C N Martin, Karen O'Connor, Ruowang Li, Pei-Chen Peng, Tiffani J Bright, Nicholas Tatonetti, Kyoung Jae Won, Graciela Gonzalez-Hernandez, Jason H Moore

The introduction of large language models (LLMs) that allow iterative "chat" in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic's perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.

2022 年末引入的大型语言模型(LLM)允许迭代式 "聊天",这是一种范式的转变,它能生成与人类所写文本无异的文本。基于 LLM 的聊天机器人在提高学术工作效率方面潜力巨大,但必须考虑其公平使用和固有偏见的伦理影响。在这篇社论中,我们从学者的角度讨论了这项技术在学术写作、教育和编程方面的局限性和实用性。最后,我们对在学术界使用 LLM 和聊天机器人的立场总结如下:(1)我们必须找到有效使用它们的方法;(2)使用它们并不构成剽窃(尽管它们可能会产生剽窃文本);(3)我们必须量化它们的偏见;(4)用户必须警惕它们的低准确性;(5)它们作为学术工具应用于研究的前景是光明的。
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引用次数: 0
Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface. 多主体多类别运动图像脑机接口的重叠滤波组卷积神经网络。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-07-11 DOI: 10.1186/s13040-023-00336-y
Jing Luo, Jundong Li, Qi Mao, Zhenghao Shi, Haiqin Liu, Xiaoyong Ren, Xinhong Hei

Background: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.

Methods: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.

Results: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.

Conclusion: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

背景:运动图像脑机接口(BCI)是实现脑机集成的一种经典的、有潜力的脑机接口技术。在运动图像脑机接口中,脑电信号的工作频带对运动图像脑电识别模型的性能影响很大。然而,由于大多数算法使用的是较宽的频带,因此没有充分利用多子带的识别能力。因此,利用卷积神经网络(cnn)从不同频率分量的脑电信号中提取判别特征是一种很有前途的多主体脑电信号识别方法。方法:本文提出了一种新的重叠滤波组CNN,用于多主体运动图像识别。具体而言,采用固定低截止频率或滑动低截止频率的两个重叠滤波器组来获得脑电信号的多频率分量表示。然后,分别训练多个CNN模型。最后,综合多个CNN模型的输出概率,确定预测的脑电标签。结果:基于四种流行的CNN主干模型和三种公开数据集进行了实验。结果表明,重叠滤波组CNN在提高多主体运动图像脑机接口性能方面是有效和通用的。具体而言,与原主干模型相比,该方法平均准确率提高3.69个百分点,F1分数提高0.04个百分点,AUC提高0.03个百分点。此外,所提出的方法在与最先进的方法的比较中表现最好。结论:提出的固定低频重叠滤波组CNN框架是提高多主体运动意象脑机接口性能的一种有效且通用的方法。
{"title":"Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.","authors":"Jing Luo,&nbsp;Jundong Li,&nbsp;Qi Mao,&nbsp;Zhenghao Shi,&nbsp;Haiqin Liu,&nbsp;Xiaoyong Ren,&nbsp;Xinhong Hei","doi":"10.1186/s13040-023-00336-y","DOIUrl":"https://doi.org/10.1186/s13040-023-00336-y","url":null,"abstract":"<p><strong>Background: </strong>Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.</p><p><strong>Methods: </strong>This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.</p><p><strong>Results: </strong>Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.</p><p><strong>Conclusion: </strong>The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9817376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis. 癌症亚型鉴定方法与特征选择方法在组学数据分析中的比较
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-07-07 DOI: 10.1186/s13040-023-00334-0
JiYoon Park, Jae Won Lee, Mira Park

Background: Cancer subtype identification is important for the early diagnosis of cancer and the provision of adequate treatment. Prior to identifying the subtype of cancer in a patient, feature selection is also crucial for reducing the dimensionality of the data by detecting genes that contain important information about the cancer subtype. Numerous cancer subtyping methods have been developed, and their performance has been compared. However, combinations of feature selection and subtype identification methods have rarely been considered. This study aimed to identify the best combination of variable selection and subtype identification methods in single omics data analysis.

Results: Combinations of six filter-based methods and six unsupervised subtype identification methods were investigated using The Cancer Genome Atlas (TCGA) datasets for four cancers. The number of features selected varied, and several evaluation metrics were used. Although no single combination was found to have a distinctively good performance, Consensus Clustering (CC) and Neighborhood-Based Multi-omics Clustering (NEMO) used with variance-based feature selection had a tendency to show lower p-values, and nonnegative matrix factorization (NMF) stably showed good performance in many cases unless the Dip test was used for feature selection. In terms of accuracy, the combination of NMF and similarity network fusion (SNF) with Monte Carlo Feature Selection (MCFS) and Minimum-Redundancy Maximum Relevance (mRMR) showed good overall performance. NMF always showed among the worst performances without feature selection in all datasets, but performed much better when used with various feature selection methods. iClusterBayes (ICB) had decent performance when used without feature selection.

Conclusions: Rather than a single method clearly emerging as optimal, the best methodology was different depending on the data used, the number of features selected, and the evaluation method. A guideline for choosing the best combination method under various situations is provided.

背景:癌症亚型识别对于癌症的早期诊断和提供适当的治疗非常重要。在确定患者的癌症亚型之前,通过检测包含癌症亚型重要信息的基因,特征选择对于降低数据的维数也至关重要。已经开发了许多癌症亚型方法,并对它们的性能进行了比较。然而,结合特征选择和亚型识别的方法很少被考虑。本研究旨在确定单组学数据分析中变量选择和亚型鉴定的最佳组合方法。结果:使用The Cancer Genome Atlas (TCGA)数据集对4种癌症进行了6种基于过滤器的方法和6种无监督亚型鉴定方法的组合研究。所选择的特征数量各不相同,并且使用了几种评估指标。虽然没有发现单一组合具有明显的良好性能,但共识聚类(CC)和基于邻域的多组学聚类(NEMO)与基于方差的特征选择一起使用有显示较低p值的趋势,非负矩阵分解(NMF)在许多情况下稳定地显示出良好的性能,除非使用Dip测试进行特征选择。在准确率方面,NMF和相似网络融合(SNF)与蒙特卡罗特征选择(MCFS)和最小冗余最大相关性(mRMR)相结合,整体表现良好。在没有特征选择的情况下,NMF在所有数据集中的表现都是最差的,而在与各种特征选择方法结合使用时,NMF的表现要好得多。iClusterBayes (ICB)在没有特征选择的情况下具有良好的性能。结论:最佳方法不是单一方法,而是根据所使用的数据、所选择的特征数量和评估方法而有所不同。为在各种情况下选择最佳组合方法提供了指导。
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引用次数: 0
ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization. 使用变分自编码器和扩展互信息正则化的单细胞转录数据的可解释降维。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-06-10 DOI: 10.1186/s13040-023-00333-1
Weiquan Pan, Faning Long, Jian Pan

Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by identifying cell types. Recently, advances in Variational Autoencoder (VAE) have demonstrated their ability to learn robust feature representations for scRNA-seq. However, it has been observed that VAEs tend to ignore the latent variables when combined with a decoding distribution that is too flexible. In this paper, we introduce ScInfoVAE, a dimensional reduction method based on the mutual information variational autoencoder (InfoVAE), which can more effectively identify various cell types in scRNA-seq data of complex tissues. A joint InfoVAE deep model and zero-inflated negative binomial distributed model design based on ScInfoVAE reconstructs the objective function to noise scRNA-seq data and learn an efficient low-dimensional representation of it. We use ScInfoVAE to analyze the clustering performance of 15 real scRNA-seq datasets and demonstrate that our method provides high clustering performance. In addition, we use simulated data to investigate the interpretability of feature extraction, and visualization results show that the low-dimensional representation learned by ScInfoVAE retains local and global neighborhood structure data well. In addition, our model can significantly improve the quality of the variational posterior.

单细胞rna测序(scRNA-seq)数据可以作为细胞间异质性的良好指标,并可以通过识别细胞类型来帮助研究细胞生长。最近,变分自编码器(VAE)的进展已经证明了它们能够学习scRNA-seq的鲁棒特征表示。然而,已经观察到,当与过于灵活的解码分布相结合时,VAEs倾向于忽略潜在变量。本文介绍了一种基于互信息变分自编码器(InfoVAE)的降维方法sciinfovae,该方法可以更有效地识别复杂组织scRNA-seq数据中的各种细胞类型。基于ScInfoVAE的联合InfoVAE深度模型和零膨胀负二项分布模型设计,对scRNA-seq数据重构目标函数,并学习其高效的低维表示。利用ScInfoVAE对15个真实scRNA-seq数据集的聚类性能进行了分析,结果表明该方法具有较高的聚类性能。此外,我们利用模拟数据研究了特征提取的可解释性,可视化结果表明,ScInfoVAE学习的低维表示能很好地保留局部和全局邻域结构数据。此外,我们的模型可以显著提高变分后验的质量。
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引用次数: 0
Changing word meanings in biomedical literature reveal pandemics and new technologies. 生物医学文献中不断变化的词义揭示了流行病和新技术。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-05-05 DOI: 10.1186/s13040-023-00332-2
David N Nicholson, Faisal Alquaddoomi, Vincent Rubinetti, Casey S Greene

While we often think of words as having a fixed meaning that we use to describe a changing world, words are also dynamic and changing. Scientific research can also be remarkably fast-moving, with new concepts or approaches rapidly gaining mind share. We examined scientific writing, both preprint and pre-publication peer-reviewed text, to identify terms that have changed and examine their use. One particular challenge that we faced was that the shift from closed to open access publishing meant that the size of available corpora changed by over an order of magnitude in the last two decades. We developed an approach to evaluate semantic shift by accounting for both intra- and inter-year variability using multiple integrated models. This analysis revealed thousands of change points in both corpora, including for terms such as 'cas9', 'pandemic', and 'sars'. We found that the consistent change-points between pre-publication peer-reviewed and preprinted text are largely related to the COVID-19 pandemic. We also created a web app for exploration that allows users to investigate individual terms ( https://greenelab.github.io/word-lapse/ ). To our knowledge, our research is the first to examine semantic shift in biomedical preprints and pre-publication peer-reviewed text, and provides a foundation for future work to understand how terms acquire new meanings and how peer review affects this process.

我们通常认为,词语具有固定的含义,用来描述不断变化的世界,但词语也是动态变化的。科学研究也是瞬息万变的,新概念或新方法会迅速占据人们的心智。我们研究了科学著作,包括预印本和出版前的同行评议文本,以确定已发生变化的术语并研究其使用情况。我们面临的一个特殊挑战是,从封闭式出版到开放式出版的转变意味着可用语料库的规模在过去二十年中发生了超过一个数量级的变化。我们开发了一种评估语义变化的方法,利用多种综合模型对年内和年际的变化进行考量。这项分析揭示了两个语料库中的数千个变化点,包括 "cas9"、"pandemic "和 "sars "等术语。我们发现,出版前同行评审文本和预印文本之间的一致变化点在很大程度上与 COVID-19 大流行有关。我们还创建了一个用于探索的网络应用程序,允许用户调查单个术语 ( https://greenelab.github.io/word-lapse/ )。据我们所知,我们的研究是首次研究生物医学预印本和出版前同行评审文本中的语义变化,为今后了解术语如何获得新含义以及同行评审如何影响这一过程奠定了基础。
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引用次数: 0
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare. 一种用于医疗保健高度不平衡数据分类的自检查自适应SMOTE算法(SASMOTE)。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-04-25 DOI: 10.1186/s13040-023-00330-4
Tanapol Kosolwattana, Chenang Liu, Renjie Hu, Shizhong Han, Hua Chen, Ying Lin

In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the "visible" nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.

在许多医疗保健应用中,由于目标事件(如疾病发作)的罕见发生,用于分类的数据集可能高度不平衡。SMOTE (Synthetic Minority oversampling Technique)算法通过对少数类样本进行过采样,作为一种有效的非平衡数据分类重采样方法。然而,SMOTE生成的样本可能是模糊的,低质量的,并且与大多数类别不可分离。为了提高生成样本的质量,我们提出了一种新的自检自适应SMOTE (SASMOTE)模型,该模型利用自适应最近邻选择算法来识别“可见”的最近邻,这些最近邻用于生成可能属于少数类的样本。为了进一步提高生成样本的质量,在提出的SASMOTE模型中引入了通过自检消除不确定度的方法。它的目的是过滤掉生成的样本,这些样本是高度不确定的,与大多数类是不可分割的。将该算法与现有基于smote的算法进行了比较,并通过两个现实世界的医疗案例研究证明了该算法的有效性,包括风险基因发现和致命先天性心脏病预测。通过生成更高质量的合成样本,与其他方法相比,所提出的算法能够帮助实现更好的预测性能(就F1分数而言),这有望增强机器学习模型在高度不平衡的医疗保健数据上的可用性。
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引用次数: 4
Automated quantitative trait locus analysis (AutoQTL). 自动数量性状位点分析(AutoQTL)。
IF 4.5 3区 生物学 Q1 Mathematics Pub Date : 2023-04-10 DOI: 10.1186/s13040-023-00331-3
Philip J Freda, Attri Ghosh, Elizabeth Zhang, Tianhao Luo, Apurva S Chitre, Oksana Polesskaya, Celine L St Pierre, Jianjun Gao, Connor D Martin, Hao Chen, Angel G Garcia-Martinez, Tengfei Wang, Wenyan Han, Keita Ishiwari, Paul Meyer, Alexander Lamparelli, Christopher P King, Abraham A Palmer, Ruowang Li, Jason H Moore

Background: Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.

Results: Using a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.

Conclusions: This proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.

背景:数量性状位点(QTL)分析和全基因组关联研究(GWAS)能够识别复杂性状中显著表型变异的变异。然而,选择最佳的方法,优化参数和预处理步骤需要时间和精力。尽管机器学习方法已被证明在优化和数据处理方面有很大的帮助,但由于大型异构数据集的复杂性,将它们应用于QTL分析和GWAS是具有挑战性的。在这里,我们描述了自动机器学习方法AutoQTL的概念验证,该方法能够自动执行与复杂性状分析相关的许多复杂决策,并生成描述遗传数据中存在的关系的解决方案。结果:AutoQTL利用来自褐家鼠(Rattus norvegicus)体重指数大规模GWAS的18个假定QTL的公开数据集,捕获了标准加性模型解释的表型差异。AutoQTL还检测非加性效应的证据,包括通过多个最优解在模拟数据中偏离加性和双向上位相互作用。此外,特征重要性指标提供了对多个gwas衍生的假定QTL的继承模型和预测能力的不同见解。结论:这一概念验证表明,自动化机器学习技术可以补充标准方法,并具有通过各种最优解决方案和特征重要性指标检测可加性和非可加性效应的潜力。在未来,我们的目标是通过智能特征选择和特征工程策略扩展AutoQTL以适应组学级别的数据集。
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Biodata Mining
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