作者: 言行自由 時(shí)間: 2025-3-21 22:42
,Identifying Genotype–Phenotype Correlations via Integrative Mutation Analysis,ng variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype–phenotype correlation methods by using the wealth of protein structural information.作者: 售穴 時(shí)間: 2025-3-22 01:54
Siamese Neural Networks: An Overview,cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications i作者: 大漩渦 時(shí)間: 2025-3-22 06:55 作者: 潛移默化 時(shí)間: 2025-3-22 10:37 作者: 微枝末節(jié) 時(shí)間: 2025-3-22 13:35 作者: NUDGE 時(shí)間: 2025-3-22 18:43 作者: 發(fā)炎 時(shí)間: 2025-3-22 22:26 作者: 環(huán)形 時(shí)間: 2025-3-23 02:26 作者: incision 時(shí)間: 2025-3-23 08:29 作者: FLAX 時(shí)間: 2025-3-23 12:27 作者: Tracheotomy 時(shí)間: 2025-3-23 16:54
https://doi.org/10.1007/3-540-27502-9dern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery..The capability of learning representations from structures directly without using any predefined structu作者: aspect 時(shí)間: 2025-3-23 21:37 作者: 名字的誤用 時(shí)間: 2025-3-24 00:25 作者: Phonophobia 時(shí)間: 2025-3-24 05:30 作者: 詳細(xì)目錄 時(shí)間: 2025-3-24 07:18 作者: 無(wú)彈性 時(shí)間: 2025-3-24 11:37
Hugh CartwrightIncludes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts?作者: Hiatal-Hernia 時(shí)間: 2025-3-24 16:25
Methods in Molecular Biologyhttp://image.papertrans.cn/b/image/162628.jpg作者: Panacea 時(shí)間: 2025-3-24 21:55 作者: Gourmet 時(shí)間: 2025-3-25 01:05 作者: 同謀 時(shí)間: 2025-3-25 06:06 作者: ADORN 時(shí)間: 2025-3-25 08:59
https://doi.org/10.1007/BFb0109467ng methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain’s activity or blood pressure, change over time. The objective of this chapter is to provide a gen作者: comely 時(shí)間: 2025-3-25 12:32 作者: Graphite 時(shí)間: 2025-3-25 16:43 作者: 率直 時(shí)間: 2025-3-25 22:24
Femtosekundenoptiken und -instrumente,o improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were ex作者: compel 時(shí)間: 2025-3-26 00:31
https://doi.org/10.1007/3-540-27502-9f large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated de作者: Catheter 時(shí)間: 2025-3-26 08:07
https://doi.org/10.1007/3-540-27502-9formation from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here,作者: 掙扎 時(shí)間: 2025-3-26 08:42 作者: 展覽 時(shí)間: 2025-3-26 14:03 作者: 乳汁 時(shí)間: 2025-3-26 18:40
Geschichte der Kurzzeittechnik,t of predictive models of disease risks based on personal genome sequences. To account for the complex systems within different cellular contexts, large-scale regulatory networks are critical components to be integrated into the analysis. Based on the fast accumulation of multiomics and disease gene作者: Cognizance 時(shí)間: 2025-3-26 21:00
https://doi.org/10.1007/3-540-27502-9ng the impact of the microbiome on the pathogenesis and progression of various diseases within the host. A deep learning tool, PopPhy-CNN, has been developed for the task of predicting host phenotypes using a convolutional neural network (CNN). By representing samples as annotated taxonomic trees an作者: 符合你規(guī)定 時(shí)間: 2025-3-27 03:48
Anwendungen von Femtosekundenlasern,s the identification of hot spots (HS) at protein–protein interfaces, typically conserved residues that contribute most significantly to the binding. In this chapter, we depict point-by-point an in-house pipeline used for HS prediction using only sequence-based features from the well-known SpotOn da作者: Triglyceride 時(shí)間: 2025-3-27 05:42 作者: 不吉祥的女人 時(shí)間: 2025-3-27 09:41
Klassifizierung von Femtosekundenlasern,h its sequence. We show that a partial combination of the Levenberg–Marquardt algorithm and the back-propagation algorithm produced the best results, giving the lowest error and largest Pearson correlation coefficient. We also find, as previous studies, that adding associative memory to a neural net作者: 無(wú)效 時(shí)間: 2025-3-27 15:26
Klassifizierung von Femtosekundenlasern,unts of labeled data. This chapter focuses on the prerequisite steps to the training of any algorithm: data collection and labeling. In particular, we tackle how data collection can be set up with scalability and security to avoid costly and delaying bottlenecks. Unprecedented amounts of data are no作者: outrage 時(shí)間: 2025-3-27 18:23 作者: glamor 時(shí)間: 2025-3-28 00:37
Artificial Neural Networks978-1-0716-0826-5Series ISSN 1064-3745 Series E-ISSN 1940-6029 作者: 魅力 時(shí)間: 2025-3-28 03:18 作者: ASTER 時(shí)間: 2025-3-28 06:17
Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning,ng methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain’s activity or blood pressure, change over time. The objective of this chapter is to provide a gen作者: Orthodontics 時(shí)間: 2025-3-28 14:01
Siamese Neural Networks: An Overview,aches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman’s rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types w作者: phlegm 時(shí)間: 2025-3-28 15:28
Computational Methods for Elucidating Gene Expression Regulation in Bacteria,umerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation..To understand bacteria gene expression regulation, one needs to identify promoters作者: visual-cortex 時(shí)間: 2025-3-28 19:52
Computational Approaches for De Novo Drug Design: Past, Present, and Future,o improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were ex作者: 并置 時(shí)間: 2025-3-28 23:09 作者: 圓桶 時(shí)間: 2025-3-29 03:26
Building and Interpreting Artificial Neural Network Models for Biological Systems,formation from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here,作者: Systemic 時(shí)間: 2025-3-29 10:19 作者: BUOY 時(shí)間: 2025-3-29 12:44
Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantifyignificantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, espec作者: preeclampsia 時(shí)間: 2025-3-29 17:14 作者: 啞巴 時(shí)間: 2025-3-29 20:10 作者: 共同給與 時(shí)間: 2025-3-30 02:22 作者: VOK 時(shí)間: 2025-3-30 08:02
Using Neural Networks for Relation Extraction from Biomedical Literature,understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichan作者: CIS 時(shí)間: 2025-3-30 08:43
,A Hybrid Levenberg–Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models,h its sequence. We show that a partial combination of the Levenberg–Marquardt algorithm and the back-propagation algorithm produced the best results, giving the lowest error and largest Pearson correlation coefficient. We also find, as previous studies, that adding associative memory to a neural net作者: 蘑菇 時(shí)間: 2025-3-30 14:15
Secure and Scalable Collection of Biomedical Data for Machine Learning Applications,unts of labeled data. This chapter focuses on the prerequisite steps to the training of any algorithm: data collection and labeling. In particular, we tackle how data collection can be set up with scalability and security to avoid costly and delaying bottlenecks. Unprecedented amounts of data are no作者: 教義 時(shí)間: 2025-3-30 19:50 作者: ESPY 時(shí)間: 2025-3-31 00:45
1064-3745 ation advice from the experts?.This volume presents examples of how Artificial Neural Networks (ANNs) are applied in biological sciences and related areas. Chapters cover a wide variety of topics, including the analysis of intracellular sorting information,?prediction of the behavior of bacterial co作者: RENIN 時(shí)間: 2025-3-31 01:22
https://doi.org/10.1007/3-540-27502-9ncreasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.作者: 顧客 時(shí)間: 2025-3-31 05:27
Building and Interpreting Artificial Neural Network Models for Biological Systems,ncreasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.作者: Enliven 時(shí)間: 2025-3-31 11:59 作者: Cursory 時(shí)間: 2025-3-31 16:19
Computational Methods for Elucidating Gene Expression Regulation in Bacteria,, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.作者: 終點(diǎn) 時(shí)間: 2025-3-31 18:43 作者: 一回合 時(shí)間: 2025-3-31 23:26
Femtosekundenoptiken und -instrumente,rning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.作者: Forage飼料 時(shí)間: 2025-4-1 02:10
Anwendungen von Femtosekundenlasern,ding code snippets, the main dataset, and the free and open-source modules/packages necessary for full replication of the protocol. The users should be able to develop an HS prediction model with accuracy, precision, recall, and AUROC of 0.96, 0.93, 0.91, and 0.86, respectively.作者: 物質(zhì) 時(shí)間: 2025-4-1 08:11 作者: SAGE 時(shí)間: 2025-4-1 10:34
Computational Approaches for De Novo Drug Design: Past, Present, and Future,rning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.作者: 不足的東西 時(shí)間: 2025-4-1 17:42
Predicting Hot Spots Using a Deep Neural Network Approach,ding code snippets, the main dataset, and the free and open-source modules/packages necessary for full replication of the protocol. The users should be able to develop an HS prediction model with accuracy, precision, recall, and AUROC of 0.96, 0.93, 0.91, and 0.86, respectively.作者: Admonish 時(shí)間: 2025-4-1 18:34
,A Hybrid Levenberg–Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models,nce. We propose that these fluctuations allow for better sampling. Overall we find it may be beneficial to treat different parts of a neural network with varied computational approaches during optimization.
