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The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online)最新文献

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PENN: Phase Estimation Neural Network on Gene Expression Data. PENN:基因表达数据的相位估计神经网络。
Aram Ansary Ogholbake, Qiang Cheng

With the continuous expansion of available transcriptomic data like gene expression, deep learning techniques are becoming more and more valuable in analyzing and interpreting them. The National Center for Biotechnology Information Gene Expression Omnibus (GEO) encompasses approximately 5 million gene expression datasets from animal and human subjects. Unfortunately, the majority of them do not have a recorded timestamps, hindering the exploration of the behavior and patterns of circadian genes. Therefore, predicting the phases of these unordered gene expression measurements can help understand the behavior of the circadian genes, thus providing valuable insights into the physiology, behaviors, and diseases of humans and animals. In this paper, we propose a novel approach to predict the phases of the un-timed samples based on a deep neural network architecture. It incorporates the potential periodic oscillation information of the cyclic genes into the objective function to regulate the phase estimation. To validate our method, we use mouse heart, mouse liver and temporal cortex of human brain dataset. Through our experiments, we demonstrate the effectiveness of our proposed method in predicting phases and uncovering rhythmic pattern in circadian genes.

随着可用转录组数据(如基因表达)的不断扩展,深度学习技术在分析和解释这些数据方面变得越来越有价值。国家生物技术信息中心基因表达综合数据库(GEO)包含大约500万个来自动物和人类受试者的基因表达数据集。不幸的是,它们中的大多数没有记录的时间戳,阻碍了对昼夜节律基因的行为和模式的探索。因此,预测这些无序基因表达测量的阶段可以帮助理解昼夜节律基因的行为,从而为人类和动物的生理、行为和疾病提供有价值的见解。在本文中,我们提出了一种基于深度神经网络架构的预测非定时样本相位的新方法。它将循环基因的潜在周期振荡信息纳入目标函数,以调节相位估计。为了验证我们的方法,我们使用了人类大脑数据集的小鼠心脏、小鼠肝脏和颞叶皮层。通过我们的实验,我们证明了我们提出的方法在预测昼夜节律基因的相位和揭示节律模式方面的有效性。
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引用次数: 0
The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) 第四届深度学习、大数据与区块链国际联合会议(DBB 2023)
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引用次数: 0
Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment. 利用深度学习 Manifold Alignment 探索脑电波与睡眠模式之间的联系。
Yosef Bernardus Wirian, Yang Jiang, Sylvia Cerel-Suhl, Jeremiah Suhl, Qiang Cheng

Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.

医学数据通常是多模态的,它们从不同的来源以不同的格式收集而来,如文本、图像和音频。它们在意义和语义上有一些内在联系,同时又表现出不同的外观。多导睡眠图(PSG)数据集是多模态数据,包括催眠图、心电图(ECG)和脑电图(EEG)。很难测量不同模式之间的关联。以往的研究使用 PSG 数据集来研究睡眠障碍、睡眠质量和睡眠结构之间的关系。我们利用一种新的深度学习流形配准方法来探索睡眠结构与脑电图特征之间的关系。我们的分析结果与之前使用 PSG 数据集诊断不同睡眠障碍和监测不同人群睡眠质量的研究结果一致。该方法能有效发现睡眠结构与脑电图数据集之间的关联,这对了解睡眠阶段和大脑活动的变化非常重要。另一方面,斯皮尔曼相关法作为一种常见的统计技术,却无法找到这些数据集之间的相关性。
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引用次数: 0
CNN Architectures: An Evolution CNN架构:一个进化
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
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引用次数: 0
Intel OpenVino: A Must-Know Deep Learning Toolkit Intel OpenVino:一个必须知道的深度学习工具包
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
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引用次数: 0
Machine Learning: The Fundamentals 机器学习:基础
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
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引用次数: 0
Generative Models 生成模型
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
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引用次数: 0
The Tools and the Prerequisites 工具和必备条件
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
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引用次数: 0
Interview Questions and Answers 面试问题与答案
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless computer science interview questions and answers sources. Yeah, sources about the books from countries in the world are provided.
跟随你的需要,总是满足灵感,获得大家现在很简单。连接到互联网是捷径之一。有如此多的资源提供并将我们与其他世界的情况联系起来。作为互联网上的产品之一,这个网站成为了一个非常容易找到无数计算机科学面试问答资源的地方。是的,提供了世界各国书籍的来源。
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引用次数: 0
Transfer Learning 转移学习
S. K. Vasudevan, S. Pulari, Subashri Vasudevan
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引用次数: 0
期刊
The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online)
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