scholarly journals Discerning Apical and Basolateral Properties of HT-29/B6 and IPEC-J2 Cell Layers by Impedance Spectroscopy, Mathematical Modeling and Machine Learning

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e62913 ◽  
Author(s):  
Thomas Schmid ◽  
Martin Bogdan ◽  
Dorothee Günzel
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Impedance Spectroscopy ◽  
Cell Layers ◽  
Ht 29

scholarly journals Deep Learning of Markov Model Based Machines for Determination of Better Treatment Option Decisions for Infertile Women

2019 ◽  
Author(s):  
Arni S.R. Srinivasa Rao ◽  
Michael P. Diamond
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Infertile Women ◽  
Learning Techniques ◽  
Technical Article

AbstractIn this technical article, we are proposing ideas those we have been developing of how machine learning and deep learning techniques can potentially assist obstetricians / gynecologists in better clinical decision making using infertile women in their treatment options in combination with mathematical modeling in pregnant women as examples.

scholarly journals Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

2021 ◽  
Vol 12 ◽  
Author(s):  
Renee Dale ◽  
Scott Oswald ◽  
Amogh Jalihal ◽  
Mary-Francis LaPorte ◽  
Daniel M. Fletcher ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Computational Modeling ◽  
Quantitative Methods ◽  
Plant Biology ◽  
Experimental Plant ◽  
Modeling Methods ◽  
Modeling Techniques ◽  
Research Questions ◽  
Biology Research

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

scholarly journals Bayesian combination of mechanistic modeling and machine learning (BaM3): improving clinical tumor growth predictions

2020 ◽  
Author(s):  
Pietro Mascheroni ◽  
Symeon Savvopoulos ◽  
Juan Carlos López Alfonso ◽  
Michael Meyer-Hermann ◽  
Haralampos Hatzikirou
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Tumor Growth ◽  
Predictive Accuracy ◽  
Tumor Burden ◽  
Mechanistic Modeling ◽  
Lymphocytic Leukemia ◽  
Simulated Patients ◽  
Small Subset ◽  
Reported Data

AbstractBiomedical problems are highly complex and multidimensional. Commonly, only a small subset of the relevant variables can be modeled by virtue of mathematical modeling due to lack of knowledge of the involved phenomena. Although these models are effective in analyzing the approximate dynamics of the system, their predictive accuracy is generally limited. On the other hand, statistical learning methods are well-suited for quantitative reproduction of data, but they do not provide mechanistic understanding of the investigated problem. Herein, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning (BaM3). We evaluate the proposed BaM3 method on a synthetic dataset for brain tumor growth as a proof of concept and analyze its performance in predicting two major clinical outputs, namely tumor burden and infiltration. Combining these two approaches results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation. In addition, we test the proposed methodology on a set of patients suffering from Chronic Lymphocytic Leukemia (CLL) and show excellent agreement with reported data.

scholarly journals Modeling and Optimization of Anaerobic Digestion: A Review

2021 ◽  
Vol 229 ◽  
pp. 01022
Author(s):  
Fatima Walid ◽  
Sanaa El Fkihi ◽  
Houda Benbrahim ◽  
Hicham Tagemouati
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Anaerobic Digestion ◽  
Biogas Production ◽  
Data Driven ◽  
Management Technique ◽  
Learning Models ◽  
Process Dynamics ◽  
Metaheuristic Techniques ◽  
Machine Learning Models

Anaerobic digestion is recognized as being an advantageous waste management technique representing a source of clean and renewable energy. However, biogas production through such practice is complex and it relies on the interaction of several factors including changes in operating and monitoring parameters. Enormous researchers have focused and gave their full attention to mathematical modeling of anaerobic digestion to get good insights about process dynamics, aiming to optimize its efficiency. This paper gives an overview of the different approaches applied to tackle this challenge including mechanistic and data-driven models. This review has led us to conclude that neural networks combined with metaheuristic techniques has the potential to outperform mechanistic and classical machine learning models.

Mathematical foundations

2019 ◽  
pp. 1-40
Author(s):  
Max A. Little
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Signal Processing ◽  
Mathematical Models ◽  
Applied Mathematics ◽  
Statistical Machine Learning ◽  
Mathematical Concepts ◽  
The Subject ◽  
Mathematical Foundations

Statistical machine learning and signal processing are topics in applied mathematics, which are based upon many abstract mathematical concepts. Defining these concepts clearly is the most important first step in this book. The purpose of this chapter is to introduce these foundational mathematical concepts. It also justifies the statement that much of the art of statistical machine learning as applied to signal processing, lies in the choice of convenient mathematical models that happen to be useful in practice. Convenient in this context means that the algebraic consequences of the choice of mathematical modeling assumptions are in some sense manageable. The seeds of this manageability are the elementary mathematical concepts upon which the subject is built.

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