scholarly journals Machine learning in cell biology – teaching computers to recognize phenotypes

2013 ◽  
Vol 126 (24) ◽  
pp. 5529-5539 ◽  
Author(s):  
Christoph Sommer ◽  
Daniel W. Gerlich
Keyword(s):  
Machine Learning ◽  
Cell Biology ◽  
Biology Teaching

scholarly journals A proposal of collaborative education for biochemistry and cell biology teaching

2015 ◽  
Vol 13 ◽  
pp. 30
Author(s):  
A. A. Souza-Júnior ◽  
A. P. Silva ◽  
T. A. Silva ◽  
G. P.V. Andrade
Keyword(s):  
Cell Biology ◽  
Teaching And Learning ◽  
Digital Tools ◽  
Student Interest ◽  
Creative Development ◽  
Biology Teaching ◽  
Video Lectures ◽  
The World ◽  
Information And Communication ◽  
Collaborative Education

INTRODUCTION: Currently students grow up in a world of digital tools that allow you to connect instantly with the world. At the same time, teachers face several challenges to increase student interest and learning efficiency. One such challenge is the pedagogical commitment of the density of biochemistry and cell biology contents, producing a conflict scenario, between meeting content and maintain the class quality. OBJECTIVES: From this perspective, this study aimed to evaluate the learning biochemistry and cell biology contents in high school classes of IFRN, using collaborative and digital tools in the Moodle. MATERIAL AND METHODS: The contents were offered using various tools such as video lectures, forums, questionnaires, portfolios, glossaries and electronic books. Then these tools were evaluated using an electronic form.  In addition to the tools, we evaluated the platform interaction, the performance of activities and the content gamification. RESULTS: The quantitative results revealed directly proportional relationship of the interaction of Moodle with the performance of activities. The content gamification was also assessed positively, with 61% of students considered good, very good or excellent. The best evaluated tools were video lectures, with 31% preference, and questionnaires, with 24%; followed by electronic book, with 10%, and portfolio, with 5.5%. The other tools totaled 30% of the preference. Qualitative results revealed an educational gain of content, because the student lived the experience of teaching and learning collaboratively. In addition, these tools decreased conflicts between content and schedule. CONCLUSION: Thus, the use of information and communication technology (ICT) in a collaborative learning provides relevant results, bringing the reality of the world connected to the classroom. In addition, it assists in defining the content and creative development of a strategy for the construction of the concepts applied to biochemistry and cell biology teaching.

scholarly journals Teaching a biology laboratory course using Dictyostelium

2019 ◽  
Vol 63 (8-9-10) ◽  
pp. 551-561
Author(s):  
David A. Knecht ◽  
Kate M. Cooper ◽  
Jonathan E. Moore
Keyword(s):  
Dictyostelium Discoideum ◽  
Cell Biology ◽  
Room Temperature ◽  
Model System ◽  
Educational Settings ◽  
Biology Teaching ◽  
Laboratory Course ◽  
Cellular Processes ◽  
Laboratory Courses ◽  
Biology Laboratory

The Dictyostelium discoideum model system is a powerful tool for undergraduate cell biology teaching laboratories. The cells are biologically safe, grow at room temperature and it is easy to experimentally induce, observe, and perturb a breadth of cellular processes making the system amenable to many teaching lab situations and goals. Here we outline the advantages of Dictyostelium, discuss laboratory courses we teach in three very different educational settings, and provide tips for both the novice and experienced Dictyostelium researcher. With this article and the extensive sets of protocols and tools referenced here, implementing these labs, or parts of them, will be relatively straightforward for any instructor.

scholarly journals Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes

2017 ◽  
Author(s):  
Patrick S Stumpf ◽  
Ben D MacArthur
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Regulatory Network ◽  
Cell Biology ◽  
Embryonic Stem ◽  
Network Activity ◽  
Expression Data ◽  
Cell Expression

AbstractThe molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the ‘average’ pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells – corresponding to naïve and formative pluripotent states and an early primitive endoderm state – and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell’s response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

scholarly journals The misleading certainty of uncertain data in biological network processes

2021 ◽  
Author(s):  
Michael Irvin ◽  
Arvind Ramanathan ◽  
Carlos F Lopez
Keyword(s):  
Machine Learning ◽  
Cell Biology ◽  
Quantitative Data ◽  
Ordinal Data ◽  
Mechanistic Model ◽  
Uncertain Data ◽  
Network Models ◽  
Measurement Model ◽  
Biochemical Network ◽  
Improve Model

Mathematical models are often used to study the structure and dynamics of network-driven cellular processes. In cell biology, models representing biochemical reaction networks have provided significant insights but are often plagued by a dearth of available quantitative data necessary for simulation and analysis. This has in turn led to questions about the usefulness of biochemical network models with unidentifiable parameters and high-degree of parameter sloppiness. In response, approaches to incorporate highly-available non-quantitative data and use this data to improve model certainty have been undertaken with various degrees of success. Here we employ a Bayesian inference and Machine Learning approach to first explore how quantitative and non-quantitative data can constrain a mechanistic model of apoptosis execution, in which all models can be identified. We find that two orders of magnitude more ordinal data measurements than those typically collected are necessary to achieve the same accuracy as that obtained from a quantitative dataset. We also find that ordinal and nominal non-quantitative data on their own can be combined to reduce model uncertainty and thus improve model accuracy. Further analysis demonstrates that the accuracy and certainty of model predictions strongly depends on accurate formulations of the measurement as well as the size and make-up of the nonquantitative datasets. Finally, we demonstrate the potential of a data-driven Machine Learning measurement model to identify informative mechanistic features that predict or define nonquantitative cellular phenotypes, from a systems perspective.

scholarly journals Cell Biology teaching for Biomedical students

1999 ◽  
pp. 11
Author(s):  
M. F.A. Ferreira et al.
Keyword(s):  
Annual Meeting ◽  
Cell Biology ◽  
Biology Teaching

Abstract of the panel presented at the SBBq annual meeting (see Attachment).

scholarly journals Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

2020 ◽  
Vol 31 (14) ◽  
pp. 1498-1511 ◽  
Author(s):  
Grace A. McLaughlin ◽  
Erin M. Langdon ◽  
John M. Crutchley ◽  
Liam J. Holt ◽  
M. Gregory Forest ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Heterogeneity ◽  
Particle Tracking ◽  
Cell Biology ◽  
Physical State ◽  
Biological Systems ◽  
Physical Structure ◽  
Length Scales ◽  
3D Motion ◽  
In Cells

The structure of the cytosol across different length scales is a debated topic in cell biology. Here we present tools to measure the physical state of the cytosol by analyzing the 3D motion of nanoparticles expressed in cells. We find evidence that the physical structure of the cytosol is a fundamental source of variability in biological systems.

scholarly journals Approaches to Cell Biology Teaching: A Primer on Standards

2002 ◽  
Vol 1 (4) ◽  
pp. 95-100 ◽  
Author(s):  
Kimberly Tanner ◽  
Deborah Allen
Keyword(s):  
Cell Biology ◽  
Biology Teaching

scholarly journals Approaches in Cell Biology Teaching

2002 ◽  
Vol 1 (1) ◽  
pp. 3-5 ◽  
Author(s):  
Deborah Allen ◽  
Kimberly Tanner
Keyword(s):  
Cell Biology ◽  
Biology Teaching

scholarly journals Retraction: Machine Learning based Classification for Heart Disease Identification (J. Phys.: Conf. Ser 1916 012174)

2021 ◽  
Vol 1916 (1) ◽  
pp. 012237
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Cell Biology ◽  
Learning Algorithm ◽  
Distributed Processing ◽  
Disease Classification ◽  
Disease Prediction ◽  
Similar Time ◽  
Link Type ◽  
Processing Network

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing also notes sections of this article were published in multiple other journals at a similar time [3, 4, 5, 6], by different author groups. These issues all bring the legitimacy of this article into serious doubt. The authors have not responded to confirm whether they agree or disagree to this retraction. IOP Publishing wishes to credit Problematic Paper Screener [7] for bringing some of these issues to our attention. 1. "Machine learning" Wikipedia, Wikimedia Foundation,https://en.wikipedia.org/wiki/Machine_learning 2. "Cardiovascular disease" Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Cardiovascular_disease 3. Sukanth, N. et al., 2021. Heart Disease Classification using Machine Learning Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 9(3), pp.1108-1114. 4. Karthikeyan, N. et al., 2021. Machine learning based classification models for heart disease prediction. Journal of Physics: Conference Series, 1916. 5. Priyadharshini, K. et al., 2021. Coronary Infarction Prediction Using Correlation Analysis aspects based on Parallel Distributed Processing Network. Annals of the Romanian Society for Cell Biology, 25(4), pp.2864-2869. 6. Vennila, V. et al., 2021. Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction. Annals of the Romanian Society for Cell Biology, 25(3), pp.8467-8474. 7. Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1 Retraction published: 17 December 2021

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