BMC Bioinformatics
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The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks. To facilitat...
#1Raul Aguirre-Gamboa (UMCG: University Medical Center Groningen)H-Index: 9
#2Niek de Klein (UMCG: University Medical Center Groningen)H-Index: 4
Last. Maria M Zorro (UMCG: University Medical Center Groningen)H-Index: 5
view all 28 authors...
Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We intro...
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#1Peter Hönigschmid (TUM: Technische Universität München)H-Index: 5
#2Stephan Breimann (TUM: Technische Universität München)
Last. Dmitrij Frishman (TUM: Technische Universität München)H-Index: 49
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This study is motivated by the following three considerations: a) the physico-chemical properties of transmembrane (TM) proteins are distinctly different from those of globular proteins, necessitating the development of specialized structure prediction techniques, b) for many structural features no specialized predictors for TM proteins are available at all, and c) deep learning algorithms allow to automate the feature engineering process and thus facilitate the development of multi-target metho...
#1Mihaly Koltai (French Institute of Health and Medical Research)H-Index: 2
#2Vincent Noël (French Institute of Health and Medical Research)H-Index: 5
Last. Emmanuel Barillot (French Institute of Health and Medical Research)H-Index: 51
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Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two ...
#1Kenneth Westerman (Tufts University)H-Index: 3
Last. Laurence D. Parnell (ARS: Agricultural Research Service)H-Index: 5
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Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could e...
#1Yang Qi (NPU: Northwestern Polytechnical University)H-Index: 1
#2Yang Guo (NPU: Northwestern Polytechnical University)H-Index: 3
Last. Xuequn Shang (NPU: Northwestern Polytechnical University)H-Index: 10
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Single-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes ...
#1Wei Du (Ministry of Education)H-Index: 8
#2Yu Sun (Ministry of Education)
Last. Ying Li (Ministry of Education)
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BACKGROUND Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly depe...
#1Jianwei Li (HEBUT: Hebei University of Technology)
#2Xiaoyu Ma (HEBUT: Hebei University of Technology)
Last. Junhua Gu (HEBUT: Hebei University of Technology)
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The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. ...
#1Hesham ElAbd (CAU: University of Kiel)H-Index: 1
#2Yana Bromberg (RU: Rutgers University)H-Index: 21
Last. Mareike Wendorff (CAU: University of Kiel)
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The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of t...
#1Fangjun Li (SDU: Shandong University)
#2Mu Yang (SDU: Shandong University)
Last. Dongqi Tang (SDU: Shandong University)
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Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment. In this study, we developed new strategies to extract important gene features and tr...
Top fields of study
Computer science
DNA microarray