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  1. Artikel ; Online: Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data.

    Venkat, Aarthi / Bhaskar, Dhananjay / Krishnaswamy, Smita

    Trends in immunology

    2023  Band 44, Heft 7, Seite(n) 551–563

    Abstract: Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally ... ...

    Abstract Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity.
    Mesh-Begriff(e) Humans ; Immunity ; Genomics
    Sprache Englisch
    Erscheinungsdatum 2023-06-09
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2036831-8
    ISSN 1471-4981 ; 1471-4906
    ISSN (online) 1471-4981
    ISSN 1471-4906
    DOI 10.1016/j.it.2023.05.003
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Current trends in artificial intelligence in reproductive endocrinology.

    Bhaskar, Dhananjay / Chang, T Arthur / Wang, Shunping

    Current opinion in obstetrics & gynecology

    2022  Band 34, Heft 4, Seite(n) 159–163

    Abstract: Purpose of review: Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually ... ...

    Abstract Purpose of review: Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings.
    Recent findings: Artificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.
    Summary: In this review, we discuss various applications of artificial intelligence in different areas of reproductive medicine. We summarize the current findings with their potentials and limitations, and also discuss the future direction for research and clinical applications.
    Mesh-Begriff(e) Artificial Intelligence ; Female ; Humans ; Infertility ; Machine Learning ; Pregnancy ; Reproductive Medicine
    Sprache Englisch
    Erscheinungsdatum 2022-06-27
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ZDB-ID 1049382-7
    ISSN 1473-656X ; 1040-872X
    ISSN (online) 1473-656X
    ISSN 1040-872X
    DOI 10.1097/GCO.0000000000000796
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: The need for speed: Migratory cells in tight spaces boost their molecular clock.

    Bhaskar, Dhananjay / Hruska, Alex M / Wong, Ian Y

    Cell systems

    2022  Band 13, Heft 7, Seite(n) 509–511

    Abstract: Cells migrating in spatial confinement exhibit higher intracellular calcium levels, which increases the oscillation frequency of a "molecular clock" that synchronizes guanine nucleotide exchange factor GEF-H1 and microtubule polymerization for more ... ...

    Abstract Cells migrating in spatial confinement exhibit higher intracellular calcium levels, which increases the oscillation frequency of a "molecular clock" that synchronizes guanine nucleotide exchange factor GEF-H1 and microtubule polymerization for more frequent bursts of speed.
    Mesh-Begriff(e) Microtubules ; Rho Guanine Nucleotide Exchange Factors
    Chemische Substanzen Rho Guanine Nucleotide Exchange Factors
    Sprache Englisch
    Erscheinungsdatum 2022-07-21
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2854138-8
    ISSN 2405-4720 ; 2405-4712
    ISSN (online) 2405-4720
    ISSN 2405-4712
    DOI 10.1016/j.cels.2022.06.002
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  4. Artikel ; Online: Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.

    Bhaskar, Dhananjay / Zhang, William Y / Volkening, Alexandria / Sandstede, Björn / Wong, Ian Y

    NPJ systems biology and applications

    2023  Band 9, Heft 1, Seite(n) 43

    Abstract: Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. ... ...

    Abstract Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease.
    Mesh-Begriff(e) Animals ; Cell Adhesion ; Cell Movement ; Cluster Analysis ; Data Analysis ; Machine Learning
    Sprache Englisch
    Erscheinungsdatum 2023-09-14
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2056-7189
    ISSN (online) 2056-7189
    DOI 10.1038/s41540-023-00302-8
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel: Generative modeling of biological shapes and images using a probabilistic

    Winn-Nuñez, Emily T / Witt, Hadley / Bhaskar, Dhananjay / Huang, Ryan Y / Reichner, Jonathan S / Wong, Ian Y / Crawford, Lorin

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Understanding morphological variation is an important task in many areas of computational biology. Recent studies have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best ... ...

    Abstract Understanding morphological variation is an important task in many areas of computational biology. Recent studies have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the current landscape of generative models for shapes has been mostly limited to approaches that use black-box inference-making it difficult to systematically assess the power and calibration of sub-image models. In this paper, we introduce the
    Sprache Englisch
    Erscheinungsdatum 2024-01-11
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2024.01.09.574919
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Topological data analysis of collective and individual epithelial cells using persistent homology of loops.

    Bhaskar, Dhananjay / Zhang, William Y / Wong, Ian Y

    Soft matter

    2021  Band 17, Heft 17, Seite(n) 4653–4664

    Abstract: Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without a priori information. Classically, different phases and phase transitions have ... ...

    Abstract Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without a priori information. Classically, different phases and phase transitions have been described based on local ordering, which may not capture structural features at larger length scales. Instead, topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology), and can compare different particle configurations based on the "cost" of reorganizing one configuration into another. Here, we demonstrate a topology-based machine learning approach for unsupervised profiling of individual and collective phases based on large-scale loops. We show that these topological loops (i.e. dimension 1 homology) are robust to variations in particle number and density, particularly in comparison to connected components (i.e. dimension 0 homology). We use TDA to map out phase diagrams for simulated particles with varying adhesion and propulsion, at constant population size as well as when proliferation is permitted. Next, we use this approach to profile our recent experiments on the clustering of epithelial cells in varying growth factor conditions, which are compared to our simulations. Finally, we characterize the robustness of this approach at varying length scales, with sparse sampling, and over time. Overall, we envision TDA will be broadly applicable as a model-agnostic approach to analyze active systems with varying population size, from cytoskeletal motors to motile cells to flocking or swarming animals.
    Mesh-Begriff(e) Animals ; Cytoskeleton ; Data Analysis ; Epithelial Cells ; Time
    Sprache Englisch
    Erscheinungsdatum 2021-05-04
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2191476-X
    ISSN 1744-6848 ; 1744-683X
    ISSN (online) 1744-6848
    ISSN 1744-683X
    DOI 10.1039/d1sm00072a
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: Coupling mechanical tension and GTPase signaling to generate cell and tissue dynamics.

    Zmurchok, Cole / Bhaskar, Dhananjay / Edelstein-Keshet, Leah

    Physical biology

    2018  Band 15, Heft 4, Seite(n) 46004

    Abstract: Regulators of the actin cytoskeleton such Rho GTPases can modulate forces developed in cells by promoting actomyosin contraction. At the same time, through mechanosensing, tension is known to affect the activity of Rho GTPases. What happens when these ... ...

    Abstract Regulators of the actin cytoskeleton such Rho GTPases can modulate forces developed in cells by promoting actomyosin contraction. At the same time, through mechanosensing, tension is known to affect the activity of Rho GTPases. What happens when these effects act in concert? Using a minimal model (1 GTPase coupled to a Kelvin-Voigt element), we show that two-way feedback between signaling ('RhoA') and mechanical tension (stretching) leads to a spectrum of cell behaviors, including contracted or relaxed cells, and cells that oscillate between these extremes. When such 'model cells' are connected to one another in a row or in a 2D sheet ('epithelium'), we observe waves of contraction/relaxation and GTPase activity sweeping through the tissue. The minimal model lends itself to full bifurcation analysis, and suggests a mechanism that explains behavior observed in the context of development and collective cell behavior.
    Mesh-Begriff(e) Actin Cytoskeleton/metabolism ; Actomyosin/metabolism ; Animals ; Epithelial Cells/metabolism ; Models, Biological ; Signal Transduction ; Stress, Mechanical ; rho GTP-Binding Proteins/metabolism
    Chemische Substanzen Actomyosin (9013-26-7) ; rho GTP-Binding Proteins (EC 3.6.5.2)
    Sprache Englisch
    Erscheinungsdatum 2018-04-30
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2133216-2
    ISSN 1478-3975 ; 1478-3967
    ISSN (online) 1478-3975
    ISSN 1478-3967
    DOI 10.1088/1478-3975/aab1c0
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Buch ; Online: Molecular Graph Generation via Geometric Scattering

    Bhaskar, Dhananjay / Grady, Jackson D. / Perlmutter, Michael A. / Krishnaswamy, Smita

    2021  

    Abstract: Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. Both ligand and target molecules are represented as graphs with node and edge features encoding information about atomic elements and bonds ... ...

    Abstract Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. Both ligand and target molecules are represented as graphs with node and edge features encoding information about atomic elements and bonds respectively. Although existing deep learning models perform remarkably well at predicting physicochemical properties and binding affinities, the generation of new molecules with optimized properties remains challenging. Inherently, most GNNs perform poorly in whole-graph representation due to the limitations of the message-passing paradigm. Furthermore, step-by-step graph generation frameworks that use reinforcement learning or other sequential processing can be slow and result in a high proportion of invalid molecules with substantial post-processing needed in order to satisfy the principles of stoichiometry. To address these issues, we propose a representation-first approach to molecular graph generation. We guide the latent representation of an autoencoder by capturing graph structure information with the geometric scattering transform and apply penalties that structure the representation also by molecular properties. We show that this highly structured latent space can be directly used for molecular graph generation by the use of a GAN. We demonstrate that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis.
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 006 ; 004
    Erscheinungsdatum 2021-10-12
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Buch ; Online: Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations

    Bhaskar, Dhananjay / Zhang, William Y. / Volkening, Alexandria / Sandstede, Björn / Wong, Ian Y.

    I. Clustering and Sorting with Varying Cell-Cell Adhesion

    2022  

    Abstract: Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. ... ...

    Abstract Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture based on spatial connectivity as well as regions unoccupied by cells, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease.
    Schlagwörter Quantitative Biology - Quantitative Methods
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2022-12-28
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Buch ; Online: ReLSO

    Castro, Egbert / Godavarthi, Abhinav / Rubinfien, Julian / Givechian, Kevin B. / Bhaskar, Dhananjay / Krishnaswamy, Smita

    A Transformer-based Model for Latent Space Optimization and Generation of Proteins

    2022  

    Abstract: The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed ...

    Abstract The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly-available protein datasets, including variant sets of anti-ranibizumab and GFP. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) by ReLSO compared to other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly-trained ReLSO models provides a potential avenue towards sequence-level fitness attribution information.
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-01-24
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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