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Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life…
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Denoising large-scale biological data using network filters

Large-scale biological data sets, e.g., transcriptomic, proteomic, or ecological, are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Here we describe a general method…
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Convergent selection in antibody repertoires is revealed by deep learning

Adaptive immunity is driven by the ability of lymphocytes to undergo V(D)J recombination and generate a highly diverse set of immune receptors (B cell receptors/secreted antibodies and T cell receptors) and their subsequent clonal selection and expansion upon molecular recognition of foreign antigens. These principles lead to remarkable, unique and dynamic immune receptor repertoires. Deep…
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Polynomial Phylogenetic Analysis of Tree Shapes

Phylogenetic trees are a central tool in evolutionary biology. They demonstrate evolutionary patterns among species, genes, and with modern sequencing technologies, patterns of ancestry among sets of individuals. Phylogenetic trees usually consist of tree shapes, branch lengths and partial labels. Comparing tree shapes is a main challenge in comparing phylogenetic trees as there are few…
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SIMON – machine learning software to the world

We at genular are happy to announce the release of our one of a kindMachine Learning Software – SIMON. In line with our mission of bringing reusable, user-friendly, executable and reproducible machine learning to the community, we have designed and developed SIMON as an open source automated machine learning software. All thanks to our vibrant…
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Simultaneous classification of neuroactive compounds in zebrafish

Purpose: Compounds that act on the central nervous system (CNS) are crucial tools in drug discovery and neuroscience. To discover compounds with novel mechanisms of action, researchers have developed behavioral screens in larval zebrafish including various methods to identify and classify hit compounds. However, these methods typically do not admit intuitive numerical scores of screen…
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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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USMPep: Universal Sequence Models for Major Histocompatibility Complex Binding Affinity Prediction

Background: Immunotherapy is a promising route towards personalized cancer treatment. A key algorithmic challenge in this process is to decide if a given peptide (neoepitope) binds with the major histocompatibility complex (MHC). This is an active area of research and there are many MHC binding prediction algorithms that can predict the MHC binding affinity for…
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