標(biāo)題: Titlebook: Bioinformatics Research and Applications; 17th International S Yanjie Wei,Min Li,Zhipeng Cai Conference proceedings 2021 Springer Nature Sw [打印本頁(yè)] 作者: FLAW 時(shí)間: 2025-3-21 16:27
書(shū)目名稱(chēng)Bioinformatics Research and Applications影響因子(影響力)
書(shū)目名稱(chēng)Bioinformatics Research and Applications影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Bioinformatics Research and Applications網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Bioinformatics Research and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Bioinformatics Research and Applications被引頻次
書(shū)目名稱(chēng)Bioinformatics Research and Applications被引頻次學(xué)科排名
書(shū)目名稱(chēng)Bioinformatics Research and Applications年度引用
書(shū)目名稱(chēng)Bioinformatics Research and Applications年度引用學(xué)科排名
書(shū)目名稱(chēng)Bioinformatics Research and Applications讀者反饋
書(shū)目名稱(chēng)Bioinformatics Research and Applications讀者反饋學(xué)科排名
作者: 北極人 時(shí)間: 2025-3-21 22:08
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/187168.jpg作者: biosphere 時(shí)間: 2025-3-22 03:36 作者: aquatic 時(shí)間: 2025-3-22 08:09
978-3-030-91414-1Springer Nature Switzerland AG 2021作者: 先驅(qū) 時(shí)間: 2025-3-22 09:48 作者: 占線(xiàn) 時(shí)間: 2025-3-22 13:31
https://doi.org/10.1007/978-3-031-56038-5Inflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communiti作者: 你不公正 時(shí)間: 2025-3-22 19:53 作者: 凈禮 時(shí)間: 2025-3-22 23:31 作者: 顧客 時(shí)間: 2025-3-23 04:21 作者: badinage 時(shí)間: 2025-3-23 07:55
Learning Lessons from Past Fiascoese also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group con作者: 圍裙 時(shí)間: 2025-3-23 09:53 作者: 他一致 時(shí)間: 2025-3-23 17:28 作者: 我們的面粉 時(shí)間: 2025-3-23 19:56
Understanding AI Risks and Its Impactsprevention. In this study, we propose a predictive model called TNRGCN for microbe-disease associations based on Tripartite Network and Relation Graph Convolutional Network (RGCN). Firstly, we construct a microbe-disease-drug tripartite network through data processing from four databases. Secondly, 作者: Costume 時(shí)間: 2025-3-24 00:02
https://doi.org/10.1057/9780230116122 of sepsis is challenging because individual patients respond differently to the treatment, thus calling for a pressing need of personalized treatment strategies. Reinforcement learning (RL) has been widely used to learn optimal strategies for sepsis treatment, especially for the administration of i作者: Debark 時(shí)間: 2025-3-24 03:19 作者: MUTED 時(shí)間: 2025-3-24 06:43 作者: 拋媚眼 時(shí)間: 2025-3-24 11:08
The Quest for a Better Settlementcape, local resistance, cancer development, and distant metastasis, thereby substantially affecting the future development of frontline interventions and prognosis outcomes. The molecular and cellular nature of the TIME influences disease outcome by altering the balance of suppressive versus cytotox作者: 惰性氣體 時(shí)間: 2025-3-24 16:54 作者: CONE 時(shí)間: 2025-3-24 19:36
Britain’s Search for Partners: II The UNstate-of-the-art phylogenetic methods were mostly designed for analyzing data that are significantly sparser and require extensive subsampling of strains. We present .-MSN, a novel tool that reconstructs a viral genetic relatedness network based on genetic distances, that can process hundreds of tho作者: SLAY 時(shí)間: 2025-3-25 02:25 作者: 佛刊 時(shí)間: 2025-3-25 05:25
Claudio Campagna,Daniel Guevara in biological structure and functions. However, experimental identification of succinylation sites is time-consuming and laborious. Traditional technology cannot meet the rapid growth of the sequence data sets. Therefore, we proposed a new computational method named SuccSPred to predict succinylati作者: NICHE 時(shí)間: 2025-3-25 10:37
Bioinformatics Research and Applications978-3-030-91415-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Incorporate 時(shí)間: 2025-3-25 12:24
0302-9743 nized in topical sections named: AI and disease; computational proteomics; biomedical imaging; drug screening and drug-drug interaction prediction; Biomedical data; sequencing data analysis..978-3-030-91414-1978-3-030-91415-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Intuitive 時(shí)間: 2025-3-25 16:01 作者: 性上癮 時(shí)間: 2025-3-25 20:04 作者: 仔細(xì)檢查 時(shí)間: 2025-3-26 00:48 作者: ostracize 時(shí)間: 2025-3-26 06:53 作者: manifestation 時(shí)間: 2025-3-26 11:58
Understanding AI Risks and Its Impacts function has been designed, which contains the long-term abnormal blood glucose as a penalty. The proposed model has been tested with a T1D simulator. The experimental results indicate that the introduced model is better at avoiding the blood glucose at a low level and keeping the patients on a longer duration of normal blood glucose level.作者: lambaste 時(shí)間: 2025-3-26 13:50 作者: BUST 時(shí)間: 2025-3-26 19:34
Reinforcement Learning for Diabetes Blood Glucose Control with Meal Information function has been designed, which contains the long-term abnormal blood glucose as a penalty. The proposed model has been tested with a T1D simulator. The experimental results indicate that the introduced model is better at avoiding the blood glucose at a low level and keeping the patients on a longer duration of normal blood glucose level.作者: wall-stress 時(shí)間: 2025-3-26 22:37 作者: 率直 時(shí)間: 2025-3-27 02:13
MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label utational models to explore unbeknown microbe-disease associations, rather than using the traditionally experimental method which is usually expensive and costs time, is a hot research trend. In this paper, a new method, MKL-LP, which utilizes Multiple Kernel Learning (MKL) and Label Propagation (LP作者: lambaste 時(shí)間: 2025-3-27 08:25
Immune-Microbiota Crosstalk Underlying Inflammatory Bowel DiseaseInflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communiti作者: Painstaking 時(shí)間: 2025-3-27 11:37 作者: 機(jī)械 時(shí)間: 2025-3-27 13:50 作者: conscience 時(shí)間: 2025-3-27 21:23 作者: 袖章 時(shí)間: 2025-3-28 00:19
Identification of Protein Markers Predictive of Drug-Specific Survival Outcome in Cancerse also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group con作者: 葡萄糖 時(shí)間: 2025-3-28 03:20
Diabetic Retinopathy Grading Base on Contrastive Learning and Semi-supervised Learningorithms have been proposed, their performance is still limited by the characteristics of DR lesions and grading criteria, and coarse-grained image-level label. In this paper, we propose a novel approach based on contrastive learning and semi-supervised learning to break through these limitations. We作者: 推延 時(shí)間: 2025-3-28 07:58
Reinforcement Learning for Diabetes Blood Glucose Control with Meal Informationby diet and insulin dose. The goal of blood glucose management is to continuously control the blood glucose level of a patient in a normal range. Reinforcement learning models show good effectiveness and robustness in dealing with various nonlinear control problems. Because of the importance of diet作者: Pseudoephedrine 時(shí)間: 2025-3-28 12:49
Predicting Microbe-Disease Association via Tripartite Network and Relation Graph Convolutional Netwoprevention. In this study, we propose a predictive model called TNRGCN for microbe-disease associations based on Tripartite Network and Relation Graph Convolutional Network (RGCN). Firstly, we construct a microbe-disease-drug tripartite network through data processing from four databases. Secondly, 作者: MAL 時(shí)間: 2025-3-28 15:03
Combining Model-Based and Model-Free Reinforcement Learning Policies for More Efficient Sepsis Treat of sepsis is challenging because individual patients respond differently to the treatment, thus calling for a pressing need of personalized treatment strategies. Reinforcement learning (RL) has been widely used to learn optimal strategies for sepsis treatment, especially for the administration of i作者: 歌曲 時(shí)間: 2025-3-28 20:55
An Efficient Two-Stage Fusion Network for Computer-Aided Diagnosis of Diabetic Footlack of timely diagnosis of DF. Diagnosing DF at early stage is very essential. However, it is easy for inexperienced doctors to confuse Diabetic Foot Ulcer (DFU) wounds and other specific ulcer wounds when there is a lack of patients’ health records in underdeveloped areas. In this paper, we propos作者: 背叛者 時(shí)間: 2025-3-28 23:46 作者: Indict 時(shí)間: 2025-3-29 07:02
Identification of Gastric Cancer Immune Microenvironment Related Genes with Poor Prognosis and Tumorcape, local resistance, cancer development, and distant metastasis, thereby substantially affecting the future development of frontline interventions and prognosis outcomes. The molecular and cellular nature of the TIME influences disease outcome by altering the balance of suppressive versus cytotox作者: 形容詞 時(shí)間: 2025-3-29 10:49 作者: 陳舊 時(shí)間: 2025-3-29 15:27 作者: Reclaim 時(shí)間: 2025-3-29 19:33
A Sequence-Based Antibody Paratope Prediction Model Through Combing Local-Global Information and Parn antigen, known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consumin作者: dithiolethione 時(shí)間: 2025-3-29 20:15
SuccSPred: Succinylation Sites Prediction Using Fused Feature Representation and Ranking Method in biological structure and functions. However, experimental identification of succinylation sites is time-consuming and laborious. Traditional technology cannot meet the rapid growth of the sequence data sets. Therefore, we proposed a new computational method named SuccSPred to predict succinylati作者: larder 時(shí)間: 2025-3-30 00:26 作者: 粘 時(shí)間: 2025-3-30 05:04 作者: Brochure 時(shí)間: 2025-3-30 09:13 作者: 圣人 時(shí)間: 2025-3-30 16:06 作者: GORGE 時(shí)間: 2025-3-30 16:36
Teach Meticulously and Test Rigorouslythe classification accuracy of models (LDA, DT, 1NN, SVM, RT) using our feature representation is higher than using the five acoustic features in baseline experiment, and the classification accuracy on the model (DT, 1NN) even exceeds the linguistic features of baseline experiment. The best classifi作者: bronchodilator 時(shí)間: 2025-3-30 21:23
Learning Lessons from Past Fiascoesvels are predictive of drug-specific survival outcomes. Some of the identified proteins were supported by published literature. Using the gene expression data from TCGA, we found the mRNA expression of .11% of the drug-specific proteins also showed significant correlation with drug-specific survival作者: Override 時(shí)間: 2025-3-31 04:10
Learning Lessons from Past Fiascoese-level fundus images. Meanwhile, we design a pseudo-label attention structure and deep supervision method, to increase the attention of the model to lesion features and improve the grading performance. Experiments on the open-source DR grading datasets EyePACS, Messidior, IDRiD, and FGADR can prove作者: 表皮 時(shí)間: 2025-3-31 08:48
Understanding AI Risks and Its Impactso-layer RGCN to predict microbe-disease associations. Compared with other methods, TNRGCN achieves a good performance in cross validation. Meanwhile, case studies for diseases demonstrate TNRGCN has a good performance for predicting potential microbe-disease associations.作者: 就職 時(shí)間: 2025-3-31 10:30
https://doi.org/10.1057/9780230116122model-based and model-free RL approaches to achieve more efficient personalized sepsis treatment. We demonstrate that the policy derived from our framework outperforms policies prescribed by physicians, model-based only methods, and model-free only approaches.作者: 概觀 時(shí)間: 2025-3-31 13:58
Keeping the Family Business Healthyital. In the training-validation stage, we collect 1211 images for a 5-fold cross-validation. Our method can classify DF images and non-DF images with the area under the receiver operating characteristic curve (AUC) value of 94.87., accuracy of 88.19., sensitivity of 84.79., specificity of 90.63., a作者: Agility 時(shí)間: 2025-3-31 17:46
