標(biāo)題: Titlebook: Biomedical Text Mining; Kalpana Raja Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Scienc [打印本頁(yè)] 作者: 根深蒂固 時(shí)間: 2025-3-21 17:20
書目名稱Biomedical Text Mining影響因子(影響力)
書目名稱Biomedical Text Mining影響因子(影響力)學(xué)科排名
書目名稱Biomedical Text Mining網(wǎng)絡(luò)公開(kāi)度
書目名稱Biomedical Text Mining網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Biomedical Text Mining被引頻次
書目名稱Biomedical Text Mining被引頻次學(xué)科排名
書目名稱Biomedical Text Mining年度引用
書目名稱Biomedical Text Mining年度引用學(xué)科排名
書目名稱Biomedical Text Mining讀者反饋
書目名稱Biomedical Text Mining讀者反饋學(xué)科排名
作者: fulmination 時(shí)間: 2025-3-21 22:32
https://doi.org/10.1007/978-3-662-43340-9 relevant documents for a user query are presented. The text mining protocol presented in this chapter is useful for retrieving information on drugs for patients with a specific disease. The protocol covers three major text mining tasks, namely, information retrieval, information extraction, and kno作者: jet-lag 時(shí)間: 2025-3-22 00:54 作者: LARK 時(shí)間: 2025-3-22 05:25 作者: 用不完 時(shí)間: 2025-3-22 09:28
,Erratum to: Landolt-B?rnstein,ne, and Vitamin B12, for treating both multiple sclerosis and cognitive disorder. In addition, our approach suggests six drugs for multiple sclerosis and 10 drugs for cognitive disorder. We obtained pharmacologist opinion on the drugs suggested for each condition and provided literature evidence for作者: 祝賀 時(shí)間: 2025-3-22 14:43
H. A. Alperin,G. Asch,Anne Marie Hellwegee causing genes can contribute towards biomarker discovery. This chapter presents a protocol on combining literature mining and machine learning for predicting biomedical discoveries with a special emphasis on gene–disease relation based discovery. The protocol is presented as a literature based dis作者: 完成 時(shí)間: 2025-3-22 21:05
Leitf?higkeit nichtw?sseriger L?sungenbiomedical literature databases such as PubMed. This chapter outlines a recent text mining protocol that applies natural language parsing (NLP) for named entity recognition and text processing, and support vector machines (SVM), a machine learning algorithm for classifying the processed text related作者: 容易做 時(shí)間: 2025-3-22 23:20 作者: 不可磨滅 時(shí)間: 2025-3-23 05:17
https://doi.org/10.1007/978-3-662-43342-3apted to the biomedical domain by training the language models using 28?million scientific literatures from PubMed and PubMed central. This chapter presents a protocol for relation extraction using BERT by discussing state-of-the-art for BERT versions in the biomedical domain such as BioBERT. The pr作者: deface 時(shí)間: 2025-3-23 06:03 作者: ingenue 時(shí)間: 2025-3-23 13:33
,Text Mining Protocol to Retrieve Significant Drug–Gene Interactions from PubMed Abstracts,st biomedical literature, text mining also seems to be an obvious choice which could efficiently aid with other computational methods in identifying drug–gene targets. These could aid in initial stages of reviewing the disease components or can even aid parallel in extracting drug–disease–gene/prote作者: 扔掉掐死你 時(shí)間: 2025-3-23 17:47 作者: 一夫一妻制 時(shí)間: 2025-3-23 18:25 作者: 障礙物 時(shí)間: 2025-3-23 23:51 作者: 喪失 時(shí)間: 2025-3-24 03:33
Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Infobiomedical literature databases such as PubMed. This chapter outlines a recent text mining protocol that applies natural language parsing (NLP) for named entity recognition and text processing, and support vector machines (SVM), a machine learning algorithm for classifying the processed text related作者: MUTE 時(shí)間: 2025-3-24 09:10 作者: dialect 時(shí)間: 2025-3-24 11:02 作者: 饒舌的人 時(shí)間: 2025-3-24 15:26 作者: vector 時(shí)間: 2025-3-24 19:06 作者: slow-wave-sleep 時(shí)間: 2025-3-25 02:16
https://doi.org/10.1007/978-3-662-43305-8s subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are express作者: linguistics 時(shí)間: 2025-3-25 05:07 作者: Ibd810 時(shí)間: 2025-3-25 08:45
https://doi.org/10.1007/978-3-662-43301-0ition and expertise. Furthermore, not all hypothesized markers will be borne out in a study, suggesting that high-quality initial hypotheses are crucial. In this chapter, we describe a high-throughput pipeline to produce a ranked list of high-quality hypothesized biomarkers for diseases. We review a作者: innate 時(shí)間: 2025-3-25 15:25 作者: 拋媚眼 時(shí)間: 2025-3-25 16:47 作者: 不愿 時(shí)間: 2025-3-25 20:07
https://doi.org/10.1007/978-3-662-43288-4assembly of new molecules by series of actions among the molecules. There are three important pathways in system biology studies namely signaling pathways, metabolic pathways, and genetic pathways (or) gene regulatory networks. Recently, biological pathway construction from scientific literature is 作者: 相反放置 時(shí)間: 2025-3-26 01:39
Leitf?higkeit nichtw?sseriger L?sungented approaches to process a huge volume of data on proteins and their modifications at the cellular level. The data generated at the cellular level is unique as well as arbitrary, and an accumulation of massive volume of information is inevitable. Biological research has revealed that a huge array o作者: 變態(tài) 時(shí)間: 2025-3-26 04:30
Leitf?higkeit nichtw?sseriger L?sungenion. Hundreds of PTMs act in a human cell. Among?them, only the selected PTMs are well established and documented. PubMed includes thousands of papers on the selected PTMs, and it is a challenge for the biomedical researchers to assimilate useful information manually. Alternatively, text mining appr作者: 繞著哥哥問(wèn) 時(shí)間: 2025-3-26 08:59 作者: Itinerant 時(shí)間: 2025-3-26 15:37 作者: 厭煩 時(shí)間: 2025-3-26 19:40 作者: habitat 時(shí)間: 2025-3-26 22:22 作者: 暫時(shí)中止 時(shí)間: 2025-3-27 04:46
Behavior of the Household with Land,The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more 作者: 貧困 時(shí)間: 2025-3-27 06:03 作者: 邊緣 時(shí)間: 2025-3-27 09:37
https://doi.org/10.1007/978-1-0716-2305-3drug-drug interaction; gene expression omnibus; transcriptomics; metabolomics; BioBert作者: 顛簸地移動(dòng) 時(shí)間: 2025-3-27 13:57 作者: UTTER 時(shí)間: 2025-3-27 19:13
Kalpana RajaIncludes 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: 持久 時(shí)間: 2025-3-28 02:00
Methods in Molecular Biologyhttp://image.papertrans.cn/b/image/188109.jpg作者: Anal-Canal 時(shí)間: 2025-3-28 03:43
Biomedical Text Mining978-1-0716-2305-3Series ISSN 1064-3745 Series E-ISSN 1940-6029 作者: 半球 時(shí)間: 2025-3-28 07:48 作者: Diaphragm 時(shí)間: 2025-3-28 13:11
,Text Mining Protocol to Retrieve Significant Drug–Gene Interactions from PubMed Abstracts,either genetic in nature or may be caused due to external factors. Genetic diseases are mainly the result of any anomaly in gene/protein structure or function. This disruption interferes with the normal expression of cellular components. Against external factors, even though the immunogenicity of ev作者: cardiovascular 時(shí)間: 2025-3-28 16:36
A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Ds subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are express作者: Intuitive 時(shí)間: 2025-3-28 22:12 作者: 商議 時(shí)間: 2025-3-29 02:23 作者: Altitude 時(shí)間: 2025-3-29 04:09 作者: Provenance 時(shí)間: 2025-3-29 08:57 作者: analogous 時(shí)間: 2025-3-29 12:03 作者: LAST 時(shí)間: 2025-3-29 17:39
Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Infoted approaches to process a huge volume of data on proteins and their modifications at the cellular level. The data generated at the cellular level is unique as well as arbitrary, and an accumulation of massive volume of information is inevitable. Biological research has revealed that a huge array o作者: CUB 時(shí)間: 2025-3-29 23:10
A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteion. Hundreds of PTMs act in a human cell. Among?them, only the selected PTMs are well established and documented. PubMed includes thousands of papers on the selected PTMs, and it is a challenge for the biomedical researchers to assimilate useful information manually. Alternatively, text mining appr作者: 鐵塔等 時(shí)間: 2025-3-30 01:40 作者: chassis 時(shí)間: 2025-3-30 05:56
BioBERT and Similar Approaches for Relation Extraction,. The curated information is proven to play an important role in various applications such as drug repurposing and precision medicine. Recently, due to the advancement in deep learning a transformer architecture named BERT (Bidirectional Encoder Representations from Transformers) has been proposed. 作者: 秘方藥 時(shí)間: 2025-3-30 10:28 作者: COLON 時(shí)間: 2025-3-30 12:43
,A Text Mining Protocol for Extracting Drug–Drug Interaction and Adverse Drug Reactions Specific to e comorbidities and polypharmacy. Databases such as PubMed contain hundreds of abstracts with DDI and ADR information. PubMed is being updated every day with thousands of abstracts. Therefore, manually retrieving the data and extracting the relevant information is tedious task. Hence, automated text作者: 編輯才信任 時(shí)間: 2025-3-30 19:41
Extracting Significant Comorbid Diseases from MeSH Index of PubMed,The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more 作者: 釘牢 時(shí)間: 2025-3-30 23:15
Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pats and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various作者: Ganglion 時(shí)間: 2025-3-31 02:23 作者: Negligible 時(shí)間: 2025-3-31 07:38
Book 2022se comorbidity, literature-based discovery, protocols to combine literature mining, machine learning for predicting biomedical discoveries, and uncovering unknown public knowledge by combining two pieces of information from different sets of PubMed articles. Additional chapters discuss the importanc作者: 騷動(dòng) 時(shí)間: 2025-3-31 11:40 作者: 龍蝦 時(shí)間: 2025-3-31 14:22
Landrecht und Landrechtsgesetzgebung,scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.作者: 大吃大喝 時(shí)間: 2025-3-31 21:32
A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Ly-based disease mortality in case of COVID-19 patients with type 2 diabetes mellitus (T2D), hypertension and cardiovascular disease (CVD). In this chapter, we provide a hybrid protocol based on biomedical literature mining, network analysis of omics data, and deep learning for the identification of most potential drugs for COVID-19.作者: 吞吞吐吐 時(shí)間: 2025-3-31 22:36
Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Patscale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.作者: 休閑 時(shí)間: 2025-4-1 02:00
Book 2022ch chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols...?..Authoritative and cutting-edge,?.Biomedical Text Mining .aims to be a?useful practical guide to researches to help further their studies.?? ? ? ? ?.作者: 商品 時(shí)間: 2025-4-1 08:42
https://doi.org/10.1007/978-3-662-43301-0 names, their physical and functional characteristics, and so on. The process of annotations may be classified as structural annotation, functional annotation, and relational annotation. In this chapter, a basic protocol utilizing text mining to extract biological information and predict their functional role based on Gene Ontology is provided.
