A Novel Approach for Effective Learning of Cluster Structures with Biological Data Applications

2006 ◽  
pp. 2-13
Author(s):  
Miyoung Shin
Keyword(s):  
Biological Data ◽  
Effective Learning ◽  
Novel Approach

Rescuing Training: Joining Learning and Application in a Federal Agency Training Program

1988 ◽  
Vol 17 (3) ◽  
pp. 303-313 ◽  
Author(s):  
Terry Newell ◽  
James Wolf ◽  
Allan Drexler
Keyword(s):  
Training Program ◽  
Management Training ◽  
Support Network ◽  
Senior Managers ◽  
Managerial Skills ◽  
Effective Learning ◽  
Managerial Support ◽  
Novel Approach ◽  
High Level ◽  
Formal Training Program

Traditional management training has often been too expensive or too poorly designed to be a credible vehicle for improving managerial skills and agency performance. The Senior Managers Program of the U.S. Department of Education offers an alternative. The year-long program integrates training with on-the-job application of skills to provide both a more effective learning environment and immediate results to demonstrate the value of the training. Each training workshop is followed by a planned and monitored intervention in the participant's work unit, and teams of participants work on significant agency problems identified by high-level officials. In addition to this novel approach to linking training with organizational change, the program seeks to develop a managerial support network to promote continued growth and agency improvement after the formal training program ends.

scholarly journals Metadata-Guided Visual Representation Learning for Biomedical Images

2019 ◽  
Author(s):  
Stephan Spiegel ◽  
Imtiaz Hossain ◽  
Christopher Ball ◽  
Xian Zhang
Keyword(s):  
Learning Strategies ◽  
Visual Representation ◽  
Morphological Characteristics ◽  
Representation Learning ◽  
Biological Data ◽  
Biomedical Images ◽  
Visual Appearance ◽  
Extra Effort ◽  
Novel Approach ◽  
Implicit And Explicit

AbstractMotivationThe clustering of biomedical images according to their phenotype is an important step in early drug discovery. Modern high-content-screening devices easily produce thousands of cell images, but the resulting data is usually unlabelled and it requires extra effort to construct a visual representation that supports the grouping according to the presented morphological characteristics.ResultsWe introduce a novel approach to visual representation learning that is guided by metadata. In high-context-screening, meta-data can typically be derived from the experimental layout, which links each cell image of a particular assay to the tested chemical compound and corresponding compound concentration. In general, there exists a one-to-many relationship between phenotype and compound, since various molecules and different dosage can lead to one and the same alterations in biological cells.Our empirical results show that metadata-guided visual representation learning is an effective approach for clustering biomedical images. We have evaluated our proposed approach on both benchmark and real-world biological data. Furthermore, we have juxtaposed implicit and explicit learning techniques, where both loss function and batch construction differ. Our experiments demonstrate that metadata-guided visual representation learning is able to identify commonalities and distinguish differences in visual appearance that lead to meaningful clusters, even without image-level annotations.NotePlease refer to the supplementary material for implementation details on metadata-guided visual representation learning strategies.

scholarly journals Integrated System for easier and effective Drug Information

2019 ◽  
Vol 12 (3) ◽  
pp. 1069-1077
Author(s):  
Susmitha Shankar ◽  
S. Thangam
Keyword(s):  
New Technologies ◽  
Treatment Strategies ◽  
Biological Data ◽  
Integrated System ◽  
Data Type ◽  
Drug Label ◽  
Novel Approach ◽  
Label System ◽  
Single Data ◽  
Effective Visualization

With the advent of new technologies, a large amount of biological data is easily generated in comparatively cheaper cost. Prior to this data integration was done by simple means of database addition, with less complexity due to lesser data generated in a standardized format. Understanding a complete biological phenomenon, such as disease, need a comprehensive understanding of many dimensions associated with it. This information cannot be captured in a single data type format. Mandating the use of a single data type study would leave us with incomplete answers to various biological questions. Thus the development of an effective integration technique with effective visualization platform is the need of the hour. One such framework requires the identification of relevant data from the input system, storing and transforming data into the intermediary level and then mapping these data into an appropriate position in the output systems. This intermediate level helps in reducing the number of connection and repeated specification creation. Integration of drug dataset would not only reduce the propagation of incorrect and not-updated medicinal information among doctors, but it would also help build better treatment strategies. Integration of drug data and visualization technique would be a novel approach to study drugs and effect on one platform. In this work, we tried to integrate the Adverse Effects, Drug Enforcement and Drug Label data from openFDA. This integrated database is coupled with a visualization platform IDEALS, an abbreviation for Integrated Drug Events, Adverse Effect and Label System.

scholarly journals ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

2018 ◽  
Author(s):  
Hong-Dong Li ◽  
Yunpei Xu ◽  
Xiaoshu Zhu ◽  
Quan Liu ◽  
Gilbert S. Omenn ◽  
...  
Keyword(s):  
Expression Profiles ◽  
R Package ◽  
Biological Data ◽  
Supplementary Information ◽  
Consensus Clustering ◽  
Cluster Membership ◽  
Link Type ◽  
Novel Approach ◽  
Gene Sets ◽  
Biological Interpretation

ABSTRACTMotivationClustering analysis is essential for understanding complex biological data. In widely used methods such as hierarchical clustering (HC) and consensus clustering (CC), expression profiles of all genes are often used to assess similarity between samples for clustering. These methods output sample clusters, but are not able to provide information about which gene sets (functions) contribute most to the clustering. So interpretability of their results is limited. We hypothesized that integrating prior knowledge of annotated biological processes would not only achieve satisfying clustering performance but also, more importantly, enable potential biological interpretation of clusters.ResultsHere we report ClusterMine, a novel approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets, e.g., in Gene Ontology. In addition to outputting cluster membership of each sample as conventional approaches do, it outputs gene sets that are most likely to contribute to the clustering, a feature facilitating biological interpretation. Using three cancer datasets, two single cell RNA-sequencing based cell differentiation datasets, one cell cycle dataset and two datasets of cells of different tissue origins, we found that ClusterMine achieved similar or better clustering performance and that top-scored gene sets prioritized by ClusterMine are biologically relevant.Implementation and availabilityClusterMine is implemented as an R package and is freely available at: www.genemine.org/[email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals Tail-Robust Quantile Normalization

2020 ◽  
Author(s):  
Eva Brombacher ◽  
Ariane Schad ◽  
Clemens Kreutz
Keyword(s):  
Mass Spectrometry ◽  
Microarray Data ◽  
Missing Values ◽  
Statistical Tests ◽  
Biological Data ◽  
Quantile Normalization ◽  
Proteomics Data ◽  
Experimental Conditions ◽  
Novel Approach ◽  
Biological Signals

AbstractHigh-throughput biological data – such as mass spectrometry-based proteomics data – suffer from systematic non-biological variance, which is introduced by systematic errors such as batch effects. This hinders the estimation of ‘real’ biological signals and, thus, decreases the power of statistical tests and biases the identification of differentially expressed sample classes. To remove such unintended variation, while retaining the biological signal of interest, the analysis workflows for mass spectrometry-based quantification typically comprises normalization steps prior to the statistical analysis of the data. Several normalization methods, such as quantile normalization, have originally been developed for microarray data. However, unlike microarray data, proteomics data may contain features, in the form of protein intensities, that are consistently highly abundant across experimental conditions and, hence, are encountered in the tails of the protein intensity distribution. If such proteins are present, statistical inferences of the intensity profiles of the normalized features are impeded through the increased number of false positive findings due to the biased estimation of the variance of the data. Thus, we developed a, freely available, novel approach: ‘tail-robust quantile normalization’. It extends the traditional quantile normalization to preserve the biological signals of features in the tails of the distribution over experimental conditions and to account for sample-dependent missing values.

scholarly journals On-line Learning and Ontological Engineering

10.28945/2775 ◽  
2004 ◽  
Author(s):  
Samuel Sambasivam ◽  
Tao Li
Keyword(s):  
Distance Learning ◽  
Learning Experience ◽  
Ontological Engineering ◽  
Effective Learning ◽  
Novel Approach ◽  
Course Material ◽  
On Line ◽  
On Line Learning ◽  
Material Interaction ◽  
Selection Of

The effectiveness of delivering course contents for distance learning depends on the organization of the course material, interaction methods and selection of exercise/test questions. The selection of questions plays a role equally important as the course presentation material. The use of multimedia may help ease the learning experience and so does the interaction among the students. A systematic approach to structure the course knowledge is perhaps the most important thing to effective learning. We adopt a novel approach to distance learning in which students are made to learn the ontology of the course through a template based approach.

scholarly journals AMASE: A framework for supporting personalised activity-based learning on the web

2014 ◽  
Vol 11 (1) ◽  
pp. 343-367 ◽  
Author(s):  
Athanasios Staikopoulos ◽  
Ian O'Keeffe ◽  
Rachael Rafter ◽  
Eddie Walsh ◽  
Bilal Yousuf ◽  
...  
Keyword(s):  
Information Systems ◽  
Learning Activities ◽  
Workflow Systems ◽  
Effective Learning ◽  
Personalised Learning ◽  
Adaptive Workflow ◽  
Web Information ◽  
Novel Approach ◽  
Web Information Systems ◽  
Technical Framework

Personalised web information systems have in recent years been evolving to provide richer and more tailored experiences for users than ever before. In order to provide even more interactive experiences as well as to address new opportunities, the next generation of Personalised web information systems needs to be capable of dynamically personalising not just web media but web services as well. In particular, eLearning provides an example of an application domain where learning activities and personalisation are of significant importance in order to provide learners with more engaging and effective learning experiences. This paper presents a novel approach and technical framework called AMASE to support the dynamic generation and enactment of Personalised Learning Activities, which uniquely entails the personalisation of media content and the personalisation of services in a unified manner. In doing so, AMASE follows a narrative approach to personalisation that combines state of the art techniques from both adaptive web and adaptive workflow systems.

Copper-dependent ATP7B up-regulation drives the resistance of TMEM16A-overexpressing head-and-neck cancer models to platinum toxicity

2019 ◽  
Vol 476 (24) ◽  
pp. 3705-3719 ◽  
Author(s):  
Avani Vyas ◽  
Umamaheswar Duvvuri ◽  
Kirill Kiselyov
Keyword(s):  
Oxidative Stress ◽  
Head And Neck ◽  
Transcriptional Activation ◽  
Platinum Resistance ◽  
Mrna Levels ◽  
Strong Positive Correlation ◽  
Platinum Compounds ◽  
Copper Chelation ◽  
Novel Approach ◽  
Cancer Models

Platinum-containing drugs such as cisplatin and carboplatin are routinely used for the treatment of many solid tumors including squamous cell carcinoma of the head and neck (SCCHN). However, SCCHN resistance to platinum compounds is well documented. The resistance to platinum has been linked to the activity of divalent transporter ATP7B, which pumps platinum from the cytoplasm into lysosomes, decreasing its concentration in the cytoplasm. Several cancer models show increased expression of ATP7B; however, the reason for such an increase is not known. Here we show a strong positive correlation between mRNA levels of TMEM16A and ATP7B in human SCCHN tumors. TMEM16A overexpression and depletion in SCCHN cell lines caused parallel changes in the ATP7B mRNA levels. The ATP7B increase in TMEM16A-overexpressing cells was reversed by suppression of NADPH oxidase 2 (NOX2), by the antioxidant N-Acetyl-Cysteine (NAC) and by copper chelation using cuprizone and bathocuproine sulphonate (BCS). Pretreatment with either chelator significantly increased cisplatin's sensitivity, particularly in the context of TMEM16A overexpression. We propose that increased oxidative stress in TMEM16A-overexpressing cells liberates the chelated copper in the cytoplasm, leading to the transcriptional activation of ATP7B expression. This, in turn, decreases the efficacy of platinum compounds by promoting their vesicular sequestration. We think that such a new explanation of the mechanism of SCCHN tumors’ platinum resistance identifies novel approach to treating these tumors.

Collecting Words: A Clinical Example of a Morphology-Focused Orthographic Intervention

2020 ◽  
Vol 51 (3) ◽  
pp. 544-560 ◽  
Author(s):  
Kimberly A. Murphy ◽  
Emily A. Diehm
Keyword(s):  
Written Language ◽  
Morphological Knowledge ◽  
English Orthography ◽  
Word Level ◽  
Novel Approach ◽  
Language Pathology ◽  
The One ◽  
Critical Intervention ◽  
Reading And Spelling ◽  
Reading And Spelling Difficulties

Purpose Morphological interventions promote gains in morphological knowledge and in other oral and written language skills (e.g., phonological awareness, vocabulary, reading, and spelling), yet we have a limited understanding of critical intervention features. In this clinical focus article, we describe a relatively novel approach to teaching morphology that considers its role as the key organizing principle of English orthography. We also present a clinical example of such an intervention delivered during a summer camp at a university speech and hearing clinic. Method Graduate speech-language pathology students provided a 6-week morphology-focused orthographic intervention to children in first through fourth grade ( n = 10) who demonstrated word-level reading and spelling difficulties. The intervention focused children's attention on morphological families, teaching how morphology is interrelated with phonology and etymology in English orthography. Results Comparing pre- and posttest scores, children demonstrated improvement in reading and/or spelling abilities, with the largest gains observed in spelling affixes within polymorphemic words. Children and their caregivers reacted positively to the intervention. Therefore, data from the camp offer preliminary support for teaching morphology within the context of written words, and the intervention appears to be a feasible approach for simultaneously increasing morphological knowledge, reading, and spelling. Conclusion Children with word-level reading and spelling difficulties may benefit from a morphology-focused orthographic intervention, such as the one described here. Research on the approach is warranted, and clinicians are encouraged to explore its possible effectiveness in their practice. Supplemental Material https://doi.org/10.23641/asha.12290687

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