Tag: machinelearning

Deep functional synthesis: a machine learning approach to gene functional enrichment

Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from…
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Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

2-photon intravital microscopy (2P-IVM) is a key technique to investigate cell migration and cell-to-cell interactions in organs and tissues of living organisms. Focusing on immunology, 2P-IVM allowed recording videos of leukocytes during the immune response, highlighting unprecedented mechanisms of the immune system. However, the automatic analysis of the acquired videos remains challenging and poorly reproducible.…
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Machine learning of stochastic gene network phenotypes

A recurrent challenge in biology is the development of predictive quantitative models because most molecular and cellular parameters have unknown values and realistic models are analytically intractable. While the dynamics of the system can be analyzed via computer simulations, substantial computational resources are often required given uncertain parameter values resulting in large numbers of parameter…
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Mother machine image analysis with MM3

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented…
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Genetic Algorithms for model refinement and rule discovery in a high-dimensional agent-based model of inflammation

Introduction: Agent-based modeling frequently used modeling method for multi-scale mechanistic modeling. However, the same properties that make agent-based models (ABMs) well suited to representing biological systems also present significant challenges with respect to their construction and calibration, particularly with respect to the large number of free parameters often present in these models. The challenge of…
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Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Signatures

Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, the results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a…
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Machine Learning Analysis of Motor Evoked Potential Time Series to Predict Disability Progression in Multiple Sclerosis

Background: Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of…
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Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, non-physiological, inconsistent, and labor-intensive to obtain. We use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed…
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Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks,…
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Biological network topology features predict gene dependencies in cancer cell lines

In this paper we explore computational approaches that enable us to identify genes that have become essential in individual cancer cell lines. Using recently published experimental cancer cell line gene essentiality data, human protein-protein interaction (PPI) network data and individual cell-line genomic alteration data we have built a range of machine learning classification models to…
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