scholarly journals Evaluation by hierarchical clustering of multiple cytokine expression after phytohemagglutinin stimulation

2016 ◽  
Vol 68 (3) ◽  
pp. 509-513 ◽  
Author(s):  
Chunhe Yang ◽  
Hongwu Du
Keyword(s):  
Hierarchical Clustering ◽  
Large Scale ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cytokine Expression ◽  
Biological Data ◽  
Clustering Method ◽  
Data Set ◽  
Suspension Array ◽  
Pha Stimulation

The hierarchical clustering method has been used for exploration of gene expression and proteomic profiles; however, little research into its application in the examination of expression of multiplecytokine/chemokine responses to stimuli has been reported. Thus, little progress has been made on how phytohemagglutinin(PHA) affects cytokine expression profiling on a large scale in the human hematological system. To investigate the characteristic expression pattern under PHA stimulation, Luminex, a multiplex bead-based suspension array, was performed. The data set collected from human peripheral blood mononuclear cells (PBMC) was analyzed using the hierarchical clustering method. It was revealed that two specific chemokines (CCL3 andCCL4) underwent significantly greater quantitative changes during induction of expression than other tested cytokines/chemokines after PHA stimulation. This result indicates that hierarchical clustering is a useful tool for detecting fine patterns during exploration of biological data, and that it can play an important role in comparative studies.

scholarly journals Exploring biological data: Mappings between ontology- and cluster-based representations

2013 ◽  
Vol 12 (3-4) ◽  
pp. 291-307 ◽  
Author(s):  
Ilir Jusufi ◽  
Andreas Kerren ◽  
Falk Schreiber
Keyword(s):  
Hierarchical Clustering ◽  
Visual Analysis ◽  
Large Data ◽  
Biological Data ◽  
Data Set ◽  
Metabolomics Data ◽  
Domain Experts ◽  
Data Points ◽  
Biological Input ◽  
Insight Into

Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This article proposes new visualization methods for this task. They allow for interactive selection and navigation to explore the data under consideration as well as visual analysis of mappings between ontology- and cluster-based space-filling representations. In this context, we discuss our approach together with specific properties of the biological input data and identify features that make our approach easily usable for domain experts.

scholarly journals Ab initio Spillover Compensation in CyTOF Data

2020 ◽  
Author(s):  
Qi Miao ◽  
Fang Wang ◽  
Jinzhuang Dou ◽  
Ramiz Iqbal ◽  
Muharrem Muftuoglu ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Large Scale ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Ground Truth ◽  
Spillover Effects ◽  
Lymphocytic Leukemia ◽  
Mouse Bone Marrow ◽  
Healthy Human ◽  
Human Cord Blood

AbstractSignal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git.

scholarly journals An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Hanjing Jiang ◽  
Yabing Huang
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Representation Learning ◽  
Biological Data ◽  
Graph Representation ◽  
Data Set ◽  
Validation Experiment ◽  
Biomolecular Network ◽  
Disease Associations ◽  
Drug Reposition

Abstract Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

scholarly journals Can Bibliographic Pointers for Known Biological Data Be Found Automatically? Protein Interactions as a Case Study

2001 ◽  
Vol 2 (4) ◽  
pp. 196-206 ◽  
Author(s):  
Christian Blaschke ◽  
Alfonso Valencia
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Biological Data ◽  
Positive Finding ◽  
Data Set ◽  
Large Scale Assessment ◽  
Low Coverage ◽  
Standard Protein

The Dictionary of Interacting Proteins(DIP) (Xenarioset al., 2000) is a large repository of protein interactions: its March 2000 release included 2379 protein pairs whose interactions have been detected by experimental methods. Even if many of these correspond to poorly characterized proteins, the result of massive yeast two-hybrid screenings, as many as 851 correspond to interactions detected using direct biochemical methods.We used information retrieval technology to search automatically for sentences in Medline abstracts that support these 851 DIP interactions. Surprisingly, we found correspondence between DIP protein pairs and Medline sentences describing their interactions in only 30% of the cases. This low coverage has interesting consequences regarding the quality of annotations (references) introduced in the database and the limitations of the application of information extraction (IE) technology to Molecular Biology. It is clear that the limitation of analyzing abstracts rather than full papers and the lack of standard protein names are difficulties of considerably more importance than the limitations of the IE methodology employed. A positive finding is the capacity of the IE system to identify new relations between proteins, even in a set of proteins previously characterized by human experts. These identifications are made with a considerable degree of precision.This is, to our knowledge, the first large scale assessment of IE capacity to detect previously known interactions: we thus propose the use of the DIP data set as a biological reference to benchmark IE systems.

scholarly journals Biological data annotation via a human-augmenting AI-based labeling system

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Douwe van der Wal ◽  
Iny Jhun ◽  
Israa Laklouk ◽  
Jeff Nirschl ◽  
Lara Richer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Microscopic Analysis ◽  
Image Data ◽  
Cell Types ◽  
Biological Data ◽  
Data Sets ◽  
Data Set ◽  
Data Annotation ◽  
Labeling System

AbstractBiology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

scholarly journals Clustering Large-Scale Data Based On Modified Affinity Propagation Algorithm

2016 ◽  
Vol 6 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Ahmed M. Serdah ◽  
Wesam M. Ashour
Keyword(s):  
Data Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Affinity Propagation ◽  
Clustering Method ◽  
Data Set ◽  
Local Cluster ◽  
Large Scale Data ◽  
Scale Data

Abstract Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.

CD2-mediated IL-12–dependent signals render human γδ-T cells resistant to mitogen-induced apoptosis, permitting the large-scale ex vivo expansion of functionally distinct lymphocytes: implications for the development of adoptive immunotherapy strategies

Blood ◽  
2000 ◽  
Vol 96 (12) ◽  
pp. 3827-3837 ◽  
Author(s):  
Richard D. Lopez ◽  
Shan Xu ◽  
Ben Guo ◽  
Robert S. Negrin ◽  
Edmund K. Waller
Keyword(s):  
T Cells ◽  
T Cell ◽  
Adoptive Immunotherapy ◽  
Large Scale ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Γδ T Cells ◽  
Cellular Immunotherapy ◽  
Induced Apoptosis

Abstract The ability of human γδ-T cells to mediate a number of in vitro functions, including innate antitumor and antiviral activity, suggests these cells can be exploited in selected examples of adoptive immunotherapy. To date, however, studies to examine such issues on a clinical scale have not been possible, owing in large measure to the difficulty of obtaining sufficient numbers of viable human γδ-T cells given their relative infrequency in readily available tissues. Standard methods used to expand human T cells often use a combination of mitogens, such as anti–T-cell receptor antibody OKT3 and interleukin (IL)-2. These stimuli, though promoting the expansion of αβ-T cells, usually do not promote the efficient expansion of γδ-T cells. CD2-mediated, IL-12–dependent signals that result in the selective expansion of human γδ-T cells from cultures of mitogen-stimulated human peripheral blood mononuclear cells are identified. It is first established that human γδ-T cells are exquisitely sensitive to apoptosis induced by T-cell mitogens OKT3 and IL-2. Next it is shown that the CD2-mediated IL-12–dependent signals, which lead to the expansion of γδ-T cells, do so by selectively protecting subsets of human γδ-T cells from mitogen-induced apoptosis. Finally, it is demonstrated that apoptosis-resistant γδ-T cells are capable of mediating significant antitumor cytotoxicity against a panel of human-derived tumor cell lines in vitro. Both the biologic and the practical implications of induced resistance to apoptosis in γδ-T cells are considered and discussed because these findings may play a role in the development of new forms of adoptive cellular immunotherapy.

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