当前共找到 1280 篇文献分享,本页显示第 1181 - 1200 篇。
1181.
孤舟蓑笠翁
(2022-04-30 23:23):
#paper DOI: 10.1126/science.1192788 science, 2011, How to Grow a Mind: Statistics, Structure, and Abstraction. 这是一篇综述,提出了在我看来比较可信的关于人脑如何学习的解释。人脑学习的一个特点是只需少量样本量(或者说数据很稀疏)就能学得很好,尤其是对因果关联的学习。作者认为学习效率高是因为用了抽象知识指导学习,并认为贝叶斯定理能很好地解释是如何用抽象知识指导学习的。而且贝叶斯方法可以有效利用多种形式的抽象知识,从而避免了传统方法需要穷举各种可能(一个个很长的数值向量)的需要。至于是如何从数据学到抽象知识的,比如是如何知道哪种形式是正确的,作者提到了各种形式(树、空间、环、次序……)都可以用graph表示,然后可以用分层贝叶斯模型来生成所需的graph,并且非参形式的分层贝叶斯模型自动蕴含了奥卡姆剃刀,只在数据需要时引入更多变量。不过,有些重要问题仍然没有被分层贝叶斯模型解决,比如学习到底是如何开始的?总得有什么作为基础吧?作者指出,有些贝叶斯建模者认为哪怕是最抽象的概念(比如因果关系的概念)原则上也是可以被学习的。作者还有一些讨论,比如什么Turing complete compositional representations,还有人脑具体如何实现贝叶斯算法,但目前不是我的兴趣(或者其实更是今晚我没有时间重新仔细看了……虽然2011年这篇文献出来的时候我就读过)。有兴趣的朋友可以直接找文献看。
Abstract:
In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do …
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In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?
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1182.
小擎子
(2022-04-30 21:50):
#paper doi: 10.1126/science.aan4236 Nature, 2017, Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients。发现肠道微生物组可以调节黑色素瘤患者对检查点免疫疗法的反应。患者分为有应答组(R)和无应答组(NR),R组的特点是肠道微生物组有高多样性和丰度的Ruminococcaceae(瘤胃球菌科)/Faecalibacterium(粪杆菌),而NR组的肠道微生物有低多样性和高相对丰度的Bacteroidales(拟杆菌)。通过小鼠模型发现,肠道微生物组通过调节免疫细胞浸润影响了抗肿瘤免疫应答。
Abstract:
Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral …
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Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti-programmed cell death 1 protein (PD-1) immunotherapy ( = 112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus nonresponders. Analysis of patient fecal microbiome samples ( = 43, 30 responders, 13 nonresponders) showed significantly higher alpha diversity ( < 0.01) and relative abundance of bacteria of the Ruminococcaceae family ( < 0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in responders, including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.
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1183.
笑对人生
(2022-04-30 21:43):
#paper The single-cell transcriptional landscape of mammalian organogenesis Cao J, et al. Nature. 2019 Feb;566(7745):496-502. doi: 10.1038/s41586-019-0969-x
该文章是利用一种名为sci-RNA-seq3超高通量单细胞测序技术,该技术一次实验可完成大约200万个细胞转录组测序建库,单人完成的时间为1周,成本为每个细胞0.01美元。该研究主要是对小鼠不同发育阶段的61个胚胎(E9.5到E13.5)的单细胞转录图谱进行了描述,该图谱命名为MOCA(mouse organogenesis cell altas)。文章虽然不是很新,但这是monocle3首次在scRNAseq(单细胞转录组测序)的应用案例,提供monocle3详尽的基本原理和分析思路。从文章的作者列表来看,也发现有monocle3软件开发者的名字。monocle3是一款用于scRNAseq拟时序分析的工具,为monocle2更新版本。虽然,monocle2是目前已发表文章的应用较为广泛的一款版本,但是它在实际使用时存在一些问题,第一,monocle2使用的细胞降维方式与seurat(一款流行的,能独立完成从细胞-基因表达矩阵到细胞降维聚类分群的scRNAseq工具)并不兼容;第二,该版本已被作者弃用并停止维护,实际应用中发现一些bug,却难以找到解决的方案。在生信分析中,如何选择软件往往是一个难题(这可能也是很多评测文章出现的原因)。作为一名工具的使用者,可以在充分理解算法原理的基础上,结合自己的研究,并通过调试,最终做出适当的选择。
Abstract:
Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external …
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Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting 'mouse organogenesis cell atlas' (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
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1184.
Vincent
(2022-04-30 21:26):
#paper https://doi.org/10.1038/s41467-020-17678-4 A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nature Comm (2020) 深度学习模型(CNN)在医学影像中有广泛的应用,最近也有研究指出可以通过病理图片来预测DNA突变和突变数,但是还没有研究关注过是否可以通过病理图片来预测基因表达,这篇文章填补了这部分空白。文章提出了一种基于多任务弱监督的深度学习模型 HE2RNA, 使用TCGA不同癌症类型数据(WSI + RNA-seq)进行训练,发现能准确预测基因的数量主要取决于训练数据集的大小,对这些被准确预测的基因进行富集分析,发现他们集中在免疫和T细胞调控,细胞周期,和癌症hallmark的通路上。最后文章还展现HE2RNA可以用于基因表达的空间可视化(预测基因在slide上表达)和提高MSI预测效果
IF:14.700Q1
Nature communications,
2020-08-03.
DOI: 10.1038/s41467-020-17678-4
PMID: 32747659
PMCID:PMC7400514
Abstract:
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to …
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Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.
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1185.
Ricardo
(2022-04-30 21:13):
#paper DOI: 10.1109/WACV51458.2022.00162. Uncertainty Learning towards Unsupervised Deformable Medical Image Registration. WACV(2022) 这篇文章没啥新意,感觉有点灌水。总而言之,在前列腺MRI图像中的配准工作,加入了分割标签作为形变场的约束,同时提出了一种基于laplace分布的模型不确定度估计的方法。嗯,没了。
Abstract:
Uncertainty estimation in medical image registration enables surgeons to evaluate the operative risk based on the trustworthiness of the registered image data thus of paramount importance for practical clinical applications. …
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Uncertainty estimation in medical image registration enables surgeons to evaluate the operative risk based on the trustworthiness of the registered image data thus of paramount importance for practical clinical applications. Despite the recent promising results obtained with deep unsupervised learning-based registration methods, reasoning about uncertainty of unsupervised registration models remains largely unexplored. In this work, we propose a predictive module to learn the registration and uncertainty in correspondence simultaneously. Our framework introduces empirical randomness and registration error based uncertainty prediction. We systematically assess the performances on two MRI datasets with different ensemble paradigms. Experimental results highlight that our proposed framework significantly improves the registration accuracy and uncertainty compared with the baseline.
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1186.
Ricardo
(2022-04-30 21:07):
#paper https://doi.org/10.1016/j.media.2020.101939 Image registration: Maximum likelihood, minimum entropy and deep learning. MIA(2021) 作者在这篇文章里给pair-wise和group-wise的配准任务提出了一个基于maximum profile likelihood (MPL)的理论框架,并利用渐进分析方法证明了基于MPL的配准过程实际上是最小化生成联合图像数据分布的联合熵(minimizes an upper bound on the joint entropy of the distribution that generates the joint image data)。通过优化闭合形式的profile likelihood,作者推导出了groupwise配准的congealing 方法。这篇文章很多看不懂的地方,后面还得慢慢读。
IF:10.700Q1
Medical image analysis,
2021-04.
DOI: 10.1016/j.media.2020.101939
PMID: 33388458
PMCID:PMC8046343
Abstract:
In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes …
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In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.
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1187.
Ricardo
(2022-04-30 20:52):
#paper https://doi.org/10.1016/j.media.2021.102292 Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance. MIA(2022) 微创颅内神经外科手术的精度可能会受到变形的脑组织结构的影响,例如,在神经内窥镜路径中,由于脑脊液的流出导致脑组织变形达10毫米。这篇文章提出了一种基于深度学习的无监督配准方法,用于术前MR和术中CT之间的配准。MR和CT之间的配准属于跨模态配准问题,由于难以衡量不同模态图像之间的相似性, 跨模态配准问题一直以来都比较难做。这篇文章的主要思路就是利用cyclegan将不同模态的图像转换成同模态图像,从而进行模态内的配准。另一方面,与其使用determistic cyclegan, 作者使用了probabilitic cyclegan,这样就可以输出模型对于预测的形变场的不准确度的估计,这种不准确度的估计可以进一步拿来作为形变场的约束。
IF:10.700Q1
Medical image analysis,
2022-01.
DOI: 10.1016/j.media.2021.102292
PMID: 34784539
PMCID:PMC10229200
Abstract:
PURPOSE: The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic …
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PURPOSE: The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance.METHOD: The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks.RESULTS: The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s.CONCLUSION: The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.
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1188.
Ricardo
(2022-04-30 20:39):
#paper https://doi.org/10.48550/arXiv.1806.09055 DARTS: differentiable architecture search ICLR(2019) Neural Architectural Search (NAS) 这个问题是出了名的消耗算力,动不动就需要消耗上千个gpu hour,基本也只能在顶级的研究机构做这类研究。这篇文章没有使用类似于进化算法或者强化学习这样的方法在离散和不可微的空间中搜索网络架构, 而是通过对神经网络的架构表征进行松弛,将NAS问题转化为一个可微分的形式,从而能够使用梯度下降法在连续空间中搜索神经网络架构。作者将这个问题建模成一个bilevel的优化问题,然后提出了一个类似于EM算法的优化方法,通过交替优化模型架构参数\alpha和模型权重w来找到较优的模型架构\alpha 。由于优化过程中涉及二阶导的计算,作者进一步对二阶导的计算做了松弛,将其转化为形式为一阶导的估计,从而进一步降低了方法的复杂度。结果也都很漂亮,相比于之前那些动辄需要上千个gpu day的计算量,darts方法只需要几个gpu day的计算,而且也能达到差不多的效果。
arXiv,
2019.
DOI: 10.48550/arXiv.1806.09055
Abstract:
This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and …
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This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms.
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1189.
张贝
(2022-04-30 20:39):
#paper DOI: 10.1038/s41580-021-00407-0 Nat Rev Mol Cell Biol.,2021,A guide to machine learning for biologists. 近几十年来,随着生物数据集规模与复杂性的大幅增长,机器学习越来越多的用于为潜在生物过程构建信息与预测模型。然而具体的机器学习方法多种多样,令人眼花缭乱。对于不同类型的生物数据,该如何选择特定的机器学习技术?本文是一篇2021年发表在Nature Reviews Molecular Cell Biology 上的综述文章,向读者简要介绍了一些关键的机器学习技术:既包括分类、回归、聚类模型等传统机器学习方法,也包括最近开发和广泛使用的涉及深度神经网络的技术。本文描述了不同的技术如何适用于特定类型的生物学数据,并指出着手进行涉及机器学习的实验时需要考虑的要点。最后,本文还讨论了一些机器学习研究的新方向。
Abstract:
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. …
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The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
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1190.
颜林林
(2022-04-30 18:41):
#paper doi:10.1016/j.ccell.2022.04.002 Cancer Cell, 2022, The translational challenges of precision oncology. 这是一篇新近发表在Cancer Cell上的关于精准肿瘤学(precision oncology)的综述。所谓精准肿瘤学,是指基于肿瘤分子特征进行肿瘤诊治决策。这篇综述回顾了与肿瘤分子特征相关的研究历史和当前研究进展,从肿瘤发生、肿瘤预防、早期检测、新辅助治疗、微小病变残留监测、药物耐受、肿瘤演化过程、肿瘤转移等诊治不同阶段环节,讨论了相应重要分子特征的发现及应用。本文对于目前在肿瘤基因检测行业中涉及到的各类应用,包括涉及的临床队列研究和相关资源,都有提及,整体上内容全面、逻辑脉络清晰。比较适合初学者,快速了解这个方向的产业应用和临床应用,并强烈建议可追溯其参考文献,对各个具体应用场景,进行深入探索和学习。
Abstract:
The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing …
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The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing efforts and discoveries that have further supported this notion. In this review, we reflect on the impact of these discoveries on our understanding of several concepts: cancer initiation, cancer prevention, early detection, adjuvant therapy and minimal residual disease monitoring, cancer drug resistance, and cancer evolution in metastasis. We discuss key areas of focus for improving cancer outcomes, from biological insights to clinical application, and suggest where the development of these technologies will lead us. Finally, we discuss practical challenges to the wider adoption of molecular profiling in the clinic and the need for robust translational infrastructure.
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1191.
June
(2022-04-30 14:29):
#paper https://doi.org/10.1038/s41392-022-00936-w该研究通过将 RT 与核酸酶野生型 Cas9 (WT-PE) 融合来进行大规模基因组操作,设计了一种新的先导编辑系统。与传统的先导编辑器(PE2)不同,这种新系统同时在目标位点引入了一个 DSB 和一个 3' 延伸的瓣,然后通过内源机制将它们整合到基因组中。当它与配对的 pegRNA 结合时,WT-PE 实现了高效的大规模基因组编辑,包括大片段缺失和染色体易位。因此, WT-PE 系统可能有助于建模或治疗与大片段畸变相关的疾病。
IF:40.800Q1
Signal transduction and targeted therapy,
2022-04-20.
DOI: 10.1038/s41392-022-00936-w
PMID: 35440051
PMCID:PMC9018734
Abstract:
Large scale genomic aberrations including duplication, deletion, translocation, and other structural changes are the cause of a subtype of hereditary genetic disorders and contribute to onset or progress of cancer. …
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Large scale genomic aberrations including duplication, deletion, translocation, and other structural changes are the cause of a subtype of hereditary genetic disorders and contribute to onset or progress of cancer. The current prime editor, PE2, consisting of Cas9-nickase and reverse transcriptase enables efficient editing of genomic deletion and insertion, however, at small scale. Here, we designed a novel prime editor by fusing reverse transcriptase (RT) to nuclease wild-type Cas9 (WT-PE) to edit large genomic fragment. WT-PE system simultaneously introduced a double strand break (DSB) and a single 3' extended flap in the target site. Coupled with paired prime editing guide RNAs (pegRNAs) that have complementary sequences in their 3' terminus while target different genomic regions, WT-PE produced bi-directional prime editing, which enabled efficient and versatile large-scale genome editing, including large fragment deletion up to 16.8 megabase (Mb) pairs and chromosomal translocation. Therefore, our WT-PE system has great potential to model or treat diseases related to large-fragment aberrations.
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1192.
大象城南
(2022-04-30 14:19):
#paper https://doi.org/10.1002/hbm.25739 推测为血管源性的脑白质高信号(WMH)常在健康老年人群的MRI上有发现。WMH还与衰老和认知能力下降有关。本文使用包含认知健康老年人MRI数据的纵向数据集(基线N=231人,年龄范围在64~87岁之间),比较并验证了FreeSurfer (T1w)、UBO Detector (T1W + FLAIR)和FSL-BIANCA(T1w+FLAIR)三种脑白质高信号提取的算法的有效性。作为参考,我们在T1w、3D (3D) FLAIR和二维(2D) FLAIR图像中手动分割WMH,并用于评估不同自动化算法的分割精度。此外,我们评估了算法提供的WMH体积与Fazekas评分和年龄的关系。FreeSurfer低估了WMH的体积,其骰子相似系数最差(DSC = 0.434),但其WMH的体积与Fazekas得分有很强的相关性(rs = 0.73)。BIANCA在3D FLAIR图像中实现了最高DSC(0.602)。然而,在2D FLAIR图像中(rs = 0.41),与Fazekas得分的关系仅为中等,在探索人体内轨迹时检测到许多异常值WMH体积(2D FLAIR: ~30%)。UBO Detector在DSC中与BIANCA在两种模式下的表现相似,在2D FLAIR(0.531)中达到了最佳DSC,无需定制训练数据集。此外,它与Fazekas评分有很高的相关性(2D FLAIR: rs = 0.80)。总之,我们的结果强调了仔细考虑选择的WMH分割算法和mr模态的重要性。
Abstract:
White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and …
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White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (r = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (r = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: r = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.
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1193.
吴增丁
(2022-04-29 16:29):
#paper https://doi.org/10.1038/s43018-022-00356-3
Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology (2022)
最近为了能从bulkRNAseq数据中分析出肿瘤一致性,所以在找一款比较好用的cellar deconvolution的软件。这方面引用量最好的是CIBERSORT及CIBERSORTx,但是这两款软件存在显示的缺点是只能online分析,不能本地化部署。看到前几天(2022年4月25日)刚在Nature Cancer上发表的BayesPrism,它可以本地化部署且提供的源码,赶紧读一读且拿来了试用。
该软件采用了贝叶斯统计模型,利用已经对cell type/ cell states注释过的single cell data作为 Reference,实现了从bulk RNAseq中推断出不同肿瘤细胞的组成及比例,而且还估计除了不同cell type的gene expression。而且从文章自己展示的性能看,已经超过了CIBERSORT/CIBERSORTx/Bisque/MUSiC 了,并且在肿瘤细胞10%以上的样本中,得到的表达谱和真实表达谱相关性大于0.9。
Abstract:
Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction …
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Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.
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1194.
尹志
(2022-04-28 22:10):
#paper https://doi.org/10.48550/arXiv.1503.03585 Deep Unsupervised Learning using Nonequilibrium Thermodynamics ICML (2015). 这是一篇还没完全看懂的论文,但是非常有意思。说起这篇文章的扩散模型大家一不定熟悉,但是提到最近大火的openai的工作dall-e 2,大家可能会更熟悉一点。对,Dall-E 2最早的启发就是这篇文章。本文受非平衡热力学的启发,设计了一个称之为扩散模型(diffusion model)的生成模型。我们知道,在机器学习中,对一堆数据的分布进行估计是一个极具挑战的事情。特别是要兼顾模型的灵活性(flexible)和过程的可解性(tractable)。如果把建模隐变量z到观测量x的映射作为任务,那么扩散模型的想法是,
假设整个映射是一个马尔科夫链(MC),然后数据的初始状态是由一步步不断添加高斯噪声,最终获得某种最终形态,那么反过来,可以将去噪的过程看做是生成的过程。我们针对这个MC过程进行训练,那么逆过程则可以作为生成模型生成符合分布的数据。是的,很像VAE。考虑到这类生成模型通过不断的改进,已经达到Dall-E 2的效果,值得我们深入理解背后的机制,以及是否可以在数据合成上产生更好的效果。
arXiv,
2015.
DOI: 10.48550/arXiv.1503.03585
Abstract:
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. …
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A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.
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1195.
李翛然
(2022-04-28 21:30):
#paper Wohlert and Edholm (2006) J. Chem. Phys. 125: 204703
Dynamics in atomistic simulations of phospholipid membranes: Nuclear magnetic resonance relaxation rates and lateral diffusion 本论文提出了不同脂分子的面积,相转变温度,等各种参数的得出方法,并列举出了模拟当中的一些参数选择背后的原因。最近正在深耕细挖分子动力学,因为接下来要和量子计算进行合作了,需要充分理解目前的原理及参数选择,以便找到一个最适合用量子计算的场景。
Abstract:
It is shown that a long, near microsecond, atomistic simulation can shed some light upon the dynamical processes occurring in a lipid bilayer. The analysis focuses on reorientational dynamics of …
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It is shown that a long, near microsecond, atomistic simulation can shed some light upon the dynamical processes occurring in a lipid bilayer. The analysis focuses on reorientational dynamics of the chains and lateral diffusion of lipids. It is shown that the reorientational correlation functions exhibits an algebraic decay (rather than exponential) for several orders of magnitude in time. The calculated nuclear magnetic resonance relaxation rates agree with experiments for carbons at the C7 position while there are some differences for C3. Lateral diffusion can be divided into two stages. In a first stage occurring at short times, t<5 ns, the center of mass of the lipid moves due to conformational changes of the chains while the headgroup position remains relatively fixed. In this stage, the center of mass can move up to approximately 0.8 nm. The fitted short-time diffusion coefficient is D(1)=13 x 10(-7) cm(2) s(-1) On a longer time scale, the diffusion coefficient becomes D(2)=0.79 x 10(-7) cm(2) s(-1).
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1196.
小W
(2022-04-28 10:15):
#paper doi.org/10.1038/s43018-022-00352-7 Concurrent delivery of immune checkpoint blockade modulates T cell dynamics to enhance neoantigen vaccine-generated antitumor immunity Nat Cancer (2022). 一篇介绍新生抗原疫苗(ADP 依赖性葡糖激酶突变的 9 聚体 (ADPGK))和免疫检查点抑制剂联合治疗的文章。作者通过对 MC38 模型进行新抗原疫苗接种、抗 PD-L1 治疗和联合治疗期间 DLN 和肿瘤组织中的 T 细胞进行了单细胞 RNA 测序 (scRNA-seq)。通过聚类表征,对簇间 TCR 的相似性 、克隆共享迁移分数、T 细胞进化轨迹、迁移抑制等分析跟踪 TME 的时空状态转换,1.验证了联合治疗通过对 TME 诱导 防止 Teff 细胞向终末耗竭 T 细胞的转变,以及 来自 DLN 的新浸润新抗原特异 T 细胞迁移对促进持久的免疫反应至关重要2.另一种 MC38 表位特异的 T 细胞百分比在联合治疗后也显着增加,Teff 细胞可能在原始肿瘤抗原初始引发后经历表位扩散。3.使用 IFN-γ 途径中的三个基因:Ifngr1、Zfp36l2 和 Gimap4 和两个趋化因子相关基因(Ccl5 和CXCR3) 刻画 MC38 癌症模型 ADPGK 新抗原特异性 T 细胞 (CAST) 评分,ICB 治疗增加了四分之三患者的 CAST 评分, ICB 在扩大 TME 中抗原特异性 T 细胞中的作用。作者分析使用了 10d 和 20d 的时间点的测序数据,从本文来看其分析和模型还是受到 小鼠模型 、疫苗特异性 、 临床数据 、时间梯度 的局限性。
免疫检查点阻断剂的同时递送可调节 T 细胞动力学,以增强新抗原疫苗产生的抗肿瘤免疫力
Abstract:
Neoantigen vaccines aiming to induce tumor-specific T cell responses have achieved promising antitumor effects in early clinical trials. However, the underlying mechanism regarding response or resistance to this treatment is …
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Neoantigen vaccines aiming to induce tumor-specific T cell responses have achieved promising antitumor effects in early clinical trials. However, the underlying mechanism regarding response or resistance to this treatment is unclear. Here we observe that neoantigen vaccine-generated T cells can synergize with the immune checkpoint blockade for effective tumor control. Specifically, we performed single-cell sequencing on over 100,000 T cells and uncovered that combined therapy induces an antigen-specific CD8 T cell population with active chemokine signaling (Cxcr3/Ccl5), lower co-inhibitory receptor expression (Lag3/Havcr2) and higher cytotoxicity (Fasl/Gzma). Furthermore, generation of neoantigen-specific T cells in the draining lymph node is required for combination treatment. Signature genes of this unique population are associated with T cell clonal frequency and better survival in humans. Our study profiles the dynamics of tumor-infiltrating T cells during neoantigen vaccine and immune checkpoint blockade treatments and high-dimensionally identifies neoantigen-reactive T cell signatures for future development of therapeutic strategies.
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旨在诱导肿瘤特异性T细胞反应的新抗原疫苗在早期临床试验中取得了有希望的抗肿瘤效果。然而,关于对这种治疗的反应或耐药的潜在机制尚不清楚。在这里,我们观察到新抗原疫苗产生的T细胞可以与免疫检查点阻断协同作用,从而有效控制肿瘤。具体来说,我们对超过 100,000 个 T 细胞进行了单细胞测序,发现联合疗法诱导抗原特异性 CD8 T 细胞群,具有活性趋化因子信号传导 (Cxcr3/Ccl5)、较低的共抑制受体表达 (Lag3/Havcr2) 和更高的细胞毒性 (Fasl/Gzma)。此外,联合治疗需要在引流淋巴结中产生新抗原特异性 T 细胞。这个独特群体的特征基因与T细胞克隆频率和人类更好的存活率有关。我们的研究描绘了新抗原疫苗和免疫检查点阻断治疗过程中肿瘤浸润 T 细胞的动力学,并高维识别新抗原反应性 T 细胞特征,用于未来治疗策略的开发。
1197.
洪媛媛
(2022-04-26 18:22):
#paper Cell-free DNA methylation markers for differential diagnosis of hepatocellular
carcinoma. 2022 20:8. https://doi.org/10.1186/s12916-021-02201-3.这篇文献技术路线是DNA提取,重硫酸盐转化,单链建库,探针捕获,二代测序,数据建模(SVM和多项式逻辑回归),独立验证集测试肝癌、肝硬化、健康人互相的区分效果。分类效果是The screening model can effectively discriminate HCC patients from non-HCC controls,including liver cirrhotic patients,asymptomatic HBsAg+ and healthy individuals,achieving an AUC of 0.957 (95%CI 0.939–0.975),wherea AFP only achieved an AUC of 0.803(95%CI 0.758–0.847).
IF:7.000Q1
BMC medicine,
2022-01-14.
DOI: 10.1186/s12916-021-02201-3
PMID: 35027051
PMCID:PMC8759185
Abstract:
BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection …
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BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC).METHODS: Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples.RESULTS: Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939-0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758-0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905-0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml-1) from high risk population (AUC=0.93; 95% CI 0.892-0.969).CONCLUSIONS: We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort.TRIAL REGISTRATION: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(# NCT04383353 ).
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1198.
张浩彬
(2022-04-23 15:36):
#paper Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001
2020的DeepAR算法,个人觉得还是蛮有启发性的。相比于大部分在时序问题这种的点预测,DeepAR用概率模型的思路,在每个时间点去预测期概率分布;这其实也可能更符合显示,毕竟本身时序过程就是有非常强的随机属性,概率分布本身也更贴近本质。文章本身对鲁棒性讨论不多,但DeepAR的鲁棒性应该比较好。另外就是DeepAR自身强调的是,他可以很方便地对多个相关的序列(数千上万)个进行建模并提取其中的关系,这一点确实也是比较强的。所以作者也特别提到,仅需要少量的特征工程及超参数调整,即能获得比传统模型更好的效果。(论文中的模型对比,我个人觉得确实也相对规范)。
论文本身写得很精炼,但是因为是Amazon的论文,所以亲生儿子是用Mxnet上搭建的,用起来确实有点不太方便。Pytprch和TF倒是有实现,但是实现细节也有些魔改的地方。方便性来看,确实比不过Prophet,哈哈
Abstract:
Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are …
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Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.
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1199.
张德祥
(2022-04-19 21:45):
#paper http://dx.doi.org/10.31234/osf.io/tdw82 逆向海马体高级认知: 作者分析了生物高级slam功能是生物高级认知的基础,论文第三部分调查梳理了大量生物认知文献,论文第二部分提到了作者之前实现的类生物的slam框架,更多介绍可以参考https://mp.weixin.qq.com/s/R7doxKN6ylz7QXAIMiWQlQ
PsyArXiv,
2022.
DOI: 10.31234/osf.io/tdw82
Abstract:
Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed …
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Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S ‘design’ properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.
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1200.
张浩彬
(2022-04-19 21:09):
#paper 10.7287/peerj.preprints.3190v1 Taylor, S. J., & Letham, B. (2017, August 25). Forecasting at Scale. PeerJ Prepr 17年Facebook开源的Prophet。原理不复杂,时序分成3个部分,趋势项,周期项,节假日项。之前用在我司的一个预测模型里面,但是最近算是正儿八经的把论文给读了。Prophet被诟病最多应该还是没啥理论,尤其是趋势项的部分分解过于粗暴了,把时序上的所有点,分解的所有项都看作是t的函数,确实带有一股工业界浓浓的ML气息。虽然粗暴,但不得不说使用体验却是很好。prophet特别适用于商业时间序列的预测,并且这个包中集成了很多方便使用的工具,例如可以方便地定义节假日,方便地定于周期,中间时间序列有缺失值也不仅要,集成了异常检测识别,模型评估方法,时间序列分解图,所以说,即使不是很了解理论的人,也能够很容易使用这个包,简单而言,对数据分析师,非常友好。
PeerJ Preprints,
2017.
DOI: 10.7287/peerj.preprints.3190
Abstract:
Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and …
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Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts — especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.
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