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  1. Article ; Online: Effect of functional anisotropy on the local dynamics of polymer grafted nanoparticles.

    Iyer, Balaji V S

    Soft matter

    2022  Volume 18, Issue 33, Page(s) 6209–6221

    Abstract: End-functionalised polymer grafted nanoparticles (PGNs) form bonds when their coronas overlap. The bonded interactions between the overlapping PGNs depend on the energy of the bonds ( ...

    Abstract End-functionalised polymer grafted nanoparticles (PGNs) form bonds when their coronas overlap. The bonded interactions between the overlapping PGNs depend on the energy of the bonds (
    Language English
    Publishing date 2022-08-24
    Publishing country England
    Document type Journal Article
    ZDB-ID 2191476-X
    ISSN 1744-6848 ; 1744-683X
    ISSN (online) 1744-6848
    ISSN 1744-683X
    DOI 10.1039/d2sm00710j
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  2. Article ; Online: Hierarchies in eukaryotic genome organization: Insights from polymer theory and simulations.

    Iyer, Balaji Vs / Kenward, Martin / Arya, Gaurav

    BMC biophysics

    2011  Volume 4, Page(s) 8

    Abstract: Eukaryotic genomes possess an elaborate and dynamic higher-order structure within the limiting confines of the cell nucleus. Knowledge of the physical principles and the molecular machinery that govern the 3D organization of this structure and its ... ...

    Abstract Eukaryotic genomes possess an elaborate and dynamic higher-order structure within the limiting confines of the cell nucleus. Knowledge of the physical principles and the molecular machinery that govern the 3D organization of this structure and its regulation are key to understanding the relationship between genome structure and function. Elegant microscopy and chromosome conformation capture techniques supported by analysis based on polymer models are important steps in this direction. Here, we review results from these efforts and provide some additional insights that elucidate the relationship between structure and function at different hierarchical levels of genome organization.
    Language English
    Publishing date 2011-04-15
    Publishing country England
    Document type Journal Article
    ISSN 2046-1682
    ISSN (online) 2046-1682
    DOI 10.1186/2046-1682-4-8
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  3. Article ; Online: Hierarchies in eukaryotic genome organization

    Iyer Balaji VS / Kenward Martin / Arya Gaurav

    BMC Biophysics, Vol 4, Iss 1, p

    Insights from polymer theory and simulations

    2011  Volume 8

    Abstract: Abstract Eukaryotic genomes possess an elaborate and dynamic higher-order structure within the limiting confines of the cell nucleus. Knowledge of the physical principles and the molecular machinery that govern the 3D organization of this structure and ... ...

    Abstract Abstract Eukaryotic genomes possess an elaborate and dynamic higher-order structure within the limiting confines of the cell nucleus. Knowledge of the physical principles and the molecular machinery that govern the 3D organization of this structure and its regulation are key to understanding the relationship between genome structure and function. Elegant microscopy and chromosome conformation capture techniques supported by analysis based on polymer models are important steps in this direction. Here, we review results from these efforts and provide some additional insights that elucidate the relationship between structure and function at different hierarchical levels of genome organization.
    Keywords Biology (General) ; QH301-705.5 ; Physics ; QC1-999
    Language English
    Publishing date 2011-04-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Mechanical response of networks formed by end-functionalised spherical polymer grafted nanoparticles.

    Phukan, Monmee / Haritha, Pindi / Roy, Talem Rebeda / Iyer, Balaji V S

    Soft matter

    2022  Volume 18, Issue 45, Page(s) 8591–8604

    Abstract: ... ...

    Abstract Via
    Language English
    Publishing date 2022-11-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 2191476-X
    ISSN 1744-6848 ; 1744-683X
    ISSN (online) 1744-6848
    ISSN 1744-683X
    DOI 10.1039/d2sm01174c
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  5. Article: Self-diffusion coefficient of ring polymers in semidilute solution

    Iyer, Balaji V.S / Shanbhag, Sachin / Juvekar, Vinay A / Lele, Ashish K

    Journal of polymer science. Part B Polymer physics. 2008 Nov. 01, v. 46, no. 21

    2008  

    Abstract: In a topologically constraining environment the size of a flexible nonconcatenated ring polymer (macrocycles) and its dynamics are known to differ from that of linear polymers. Hence, the diffusion coefficient of ring polymers can be expected to be ... ...

    Abstract In a topologically constraining environment the size of a flexible nonconcatenated ring polymer (macrocycles) and its dynamics are known to differ from that of linear polymers. Hence, the diffusion coefficient of ring polymers can be expected to be different from linear chains. We present here scaling arguments for the concentration and molecular weight dependence of self-diffusion coefficient of ring polymers in semidilute solutions, and show that contrary to expectations these scaling relations are identical to what is known for linear polymers. At higher concentrations excluded volume interactions arising from possibilities of segmental overlap can become effective for large ring polymers. In this regime the diffusion coefficient of large ring polymers shows a relatively weaker dependence on concentration and molecular weight. ©2008 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 46: 2370-2379, 2008
    Language English
    Dates of publication 2008-1101
    Size p. 2370-2379.
    Publishing place Wiley Subscription Services, Inc., A Wiley Company
    Document type Article
    ZDB-ID 1473448-5
    ISSN 1099-0488 ; 0887-6266
    ISSN (online) 1099-0488
    ISSN 0887-6266
    DOI 10.1002/polb.21569
    Database NAL-Catalogue (AGRICOLA)

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  6. Article ; Online: DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.

    Li, Guangyuan / Iyer, Balaji / Prasath, V B Surya / Ni, Yizhao / Salomonis, Nathan

    Briefings in bioinformatics

    2021  Volume 22, Issue 6

    Abstract: Cytolytic T-cells play an essential role in the adaptive immune system by seeking out, binding and killing cells that present foreign antigens on their surface. An improved understanding of T-cell immunity will greatly aid in the development of new ... ...

    Abstract Cytolytic T-cells play an essential role in the adaptive immune system by seeking out, binding and killing cells that present foreign antigens on their surface. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life-threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native peptides to elicit a T-cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen alleles, for both synthetic biological applications, and to augment real training datasets. Here, we propose a beta-binomial distribution approach to derive peptide immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, K-nearest neighbors, support vector machine, Random Forest and AdaBoost) and three deep learning models (convolutional neural network (CNN), Residual Net and graph neural network) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-CoV-2). We chose the CNN as the best prediction model, based on its adaptivity for small and large datasets and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepImmuno-CNN correctly predicts which residues are most important for T-cell antigen recognition and predicts novel impacts of SARS-CoV-2 variants. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physicochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.
    MeSH term(s) Algorithms ; COVID-19/immunology ; COVID-19/virology ; Deep Learning ; Humans ; Machine Learning ; Neural Networks, Computer ; Peptides/genetics ; Peptides/immunology ; SARS-CoV-2/genetics ; SARS-CoV-2/immunology ; SARS-CoV-2/pathogenicity ; Software ; T-Lymphocytes/immunology ; T-Lymphocytes/virology
    Chemical Substances Peptides
    Language English
    Publishing date 2021-05-19
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbab160
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6262: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Book ; Online: INGENIOUS

    Renduchintala, H S V N S Kowndinya / Killamsetty, Krishnateja / Bhatia, Sumit / Aggarwal, Milan / Ramakrishnan, Ganesh / Iyer, Rishabh / Krishnamurthy, Balaji

    Using Informative Data Subsets for Efficient Pre-Training of Large Language Models

    2023  

    Abstract: A salient characteristic of large pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size. Consequently, we are ... ...

    Abstract A salient characteristic of large pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size. Consequently, we are witnessing the development of enormous models pushing the state-of-the-art. It is, however, imperative to realize that this inevitably leads to prohibitively long training times, extortionate computing costs, and a detrimental environmental impact. Significant efforts are underway to make PTLM training more efficient through innovations in model architectures, training pipelines, and loss function design, with scant attention being paid to optimizing the utility of training data. The key question that we ask is whether it is possible to train PTLMs by employing only highly informative subsets of the training data while maintaining downstream performance? Building upon the recent progress in informative data subset selection, we show how we can employ submodular optimization to select highly representative subsets of the training corpora. Our results demonstrate that the proposed framework can be applied to efficiently train multiple PTLMs (BERT, BioBERT, GPT-2) using only a fraction of data while retaining up to $\sim99\%$ of the performance of the fully-trained models.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-05-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity.

    Li, Guangyuan / Iyer, Balaji / Prasath, V B Surya / Ni, Yizhao / Salomonis, Nathan

    bioRxiv : the preprint server for biology

    2020  

    Abstract: T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new ... ...

    Abstract T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen (HLA) alleles, for both synthetic biological applications and to augment real training datasets. Here, we proposed a beta-binomial distribution approach to derive epitope immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-Cov-2). We chose the CNN model as the best prediction model based on its adaptivity for small and large datasets, and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepHLApan and IEDB, DeepImmuno-CNN further correctly predicts which residues are most important for T cell antigen recognition. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physiochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.
    Data availability: DeepImmuno Python3 code is available at https://github.com/frankligy/DeepImmuno . The DeepImmuno web portal is available from https://deepimmuno.herokuapp.com . The data in this article is available in GitHub and supplementary materials.
    Language English
    Publishing date 2020-12-24
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2020.12.24.424262
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  9. Article ; Online: maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.

    Cazares, Tareian A / Rizvi, Faiz W / Iyer, Balaji / Chen, Xiaoting / Kotliar, Michael / Bejjani, Anthony T / Wayman, Joseph A / Donmez, Omer / Wronowski, Benjamin / Parameswaran, Sreeja / Kottyan, Leah C / Barski, Artem / Weirauch, Matthew T / Prasath, V B Surya / Miraldi, Emily R

    PLoS computational biology

    2023  Volume 19, Issue 1, Page(s) e1010863

    Abstract: Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular ... ...

    Abstract Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.
    MeSH term(s) Humans ; Chromatin/genetics ; Chromatin Immunoprecipitation Sequencing ; Deoxyribonucleases/genetics ; High-Throughput Nucleotide Sequencing/methods ; Sequence Analysis, DNA/methods ; Nerve Net
    Chemical Substances Chromatin ; Deoxyribonucleases (EC 3.1.-)
    Language English
    Publishing date 2023-01-31
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010863
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  10. Article ; Online: Lattice animal model of chromosome organization.

    Iyer, Balaji V S / Arya, Gaurav

    Physical review. E, Statistical, nonlinear, and soft matter physics

    2012  Volume 86, Issue 1 Pt 1, Page(s) 11911

    Abstract: Polymer models tied together by constraints of looping and confinement have been used to explain many of the observed organizational characteristics of interphase chromosomes. Here we introduce a simple lattice animal representation of interphase ... ...

    Abstract Polymer models tied together by constraints of looping and confinement have been used to explain many of the observed organizational characteristics of interphase chromosomes. Here we introduce a simple lattice animal representation of interphase chromosomes that combines the features of looping and confinement constraints into a single framework. We show through Monte Carlo simulations that this model qualitatively captures both the leveling off in the spatial distance between genomic markers observed in fluorescent in situ hybridization experiments and the inverse decay in the looping probability as a function of genomic separation observed in chromosome conformation capture experiments. The model also suggests that the collapsed state of chromosomes and their segregation into territories with distinct looping activities might be a natural consequence of confinement.
    MeSH term(s) Animals ; Chromosomes/chemistry ; Chromosomes/genetics ; Chromosomes/ultrastructure ; Interphase/genetics ; Models, Animal ; Models, Chemical ; Models, Genetic ; Models, Molecular
    Language English
    Publishing date 2012-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1550-2376
    ISSN (online) 1550-2376
    DOI 10.1103/PhysRevE.86.011911
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