LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 46

Search options

  1. Article ; Online: In silico

    Gunsalus, Laura M / Keiser, Michael J / Pollard, Katherine S

    Cell genomics

    2023  Volume 3, Issue 10, Page(s) 100410

    Abstract: Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome wide. We developed a deep-learning strategy to quantify the ... ...

    Abstract Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome wide. We developed a deep-learning strategy to quantify the effect of any insertion, deletion, or substitution on chromatin contacts and systematically scored millions of synthetic variants. While most genetic manipulations have little impact, regions with CTCF motifs and active transcription are highly sensitive, as expected. Our unbiased screen and subsequent targeted experiments also point to noncoding RNA genes and several families of repetitive elements as CTCF-motif-free DNA sequences with particularly large effects on nearby chromatin interactions, sometimes exceeding the effects of CTCF sites and explaining interactions that lack CTCF. We anticipate that our disruption tracks may be of broad interest and utility as a measure of 3D genome sensitivity, and our computational strategies may serve as a template for biological inquiry with deep learning.
    Language English
    Publishing date 2023-09-25
    Publishing country United States
    Document type Journal Article
    ISSN 2666-979X
    ISSN (online) 2666-979X
    DOI 10.1016/j.xgen.2023.100410
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article: ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorization.

    Gunsalus, Laura M / Keiser, Michael J / Pollard, Katherine S

    bioRxiv : the preprint server for biology

    2023  

    Abstract: The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, ... ...

    Abstract The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena.
    Language English
    Publishing date 2023-11-22
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.22.568268
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article: Learning chemical sensitivity reveals mechanisms of cellular response.

    Connell, William / Garcia, Kristle / Goodarzi, Hani / Keiser, Michael J

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we developed ChemProbe, ...

    Abstract Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we developed ChemProbe, a model that predicts cellular sensitivity to hundreds of molecular probes and drugs by learning to combine transcriptomes and chemical structures. Using ChemProbe, we inferred the chemical sensitivity of cancer cell lines and tumor samples and analyzed how the model makes predictions. We retrospectively evaluated drug response predictions for precision breast cancer treatment and prospectively validated chemical sensitivity predictions in new cellular models, including a genetically modified cell line. Our model interpretation analysis identified transcriptome features reflecting compound targets and protein network modules, identifying genes that drive ferroptosis. ChemProbe is an interpretable
    Language English
    Publishing date 2023-08-28
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.08.26.554851
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: Autoregressive fragment-based diffusion for pocket-aware ligand design

    Ghorbani, Mahdi / Gendelev, Leo / Beroza, Paul / Keiser, Michael J.

    2023  

    Abstract: In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures. We employ geometric vector perceptrons to predict atom types and spatial coordinates ...

    Abstract In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures. We employ geometric vector perceptrons to predict atom types and spatial coordinates of new molecular fragments conditioned on molecular scaffolds and protein pockets. Our approach improves the local geometry of the resulting 3D molecules while maintaining high predicted binding affinity to protein targets. The model can also perform scaffold extension from user-provided starting molecular scaffold.

    Comment: Accepted, NeurIPS 2023 Generative AI and Biology Workshop. OpenReview: https://openreview.net/forum?id=E3HN48zjam
    Keywords Quantitative Biology - Biomolecules ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Physics - Chemical Physics ; Quantitative Biology - Quantitative Methods ; J.2 ; I.2.6 ; I.6.3
    Publishing date 2023-12-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: Attention-Based Learning on Molecular Ensembles

    Chuang, Kangway V. / Keiser, Michael J.

    2020  

    Abstract: The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches. We describe an end-to-end deep learning approach that ... ...

    Abstract The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches. We describe an end-to-end deep learning approach that operates directly on small-molecule conformational ensembles and identifies key conformational poses of small-molecules. Our networks leverage two levels of representation learning: 1) individual conformers are first encoded as spatial graphs using a graph neural network, and 2) sampled conformational ensembles are represented as sets using an attention mechanism to aggregate over individual instances. We demonstrate the feasibility of this approach on a simple task based on bidentate coordination of biaryl ligands, and show how attention-based pooling can elucidate key conformational poses in tasks based on molecular geometry. This work illustrates how set-based learning approaches may be further developed for small molecule-based virtual screening.
    Keywords Computer Science - Machine Learning ; Physics - Chemical Physics
    Subject code 006
    Publishing date 2020-11-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Article ; Online: Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

    Chuang, Kangway V / Keiser, Michael J

    Science (New York, N.Y.)

    2018  Volume 362, Issue 6416

    Abstract: ... ...

    Abstract Ahneman
    Language English
    Publishing date 2018-11-15
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.aat8603
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 27: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 77: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: Adversarial Controls for Scientific Machine Learning.

    Chuang, Kangway V / Keiser, Michael J

    ACS chemical biology

    2018  Volume 13, Issue 10, Page(s) 2819–2821

    Abstract: New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application ...

    Abstract New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application of machine learning to experimental research merits careful consideration. Machine learning algorithms readily exploit confounding variables and experimental artifacts instead of relevant patterns, leading to overoptimistic performance and poor model generalization. In parallel to the strong control experiments that remain a cornerstone of experimental research, we advance the concept of adversarial controls for scientific machine learning: the design of exacting and purposeful experiments to ensure that predictive performance arises from meaningful models.
    MeSH term(s) Logic ; Machine Learning/standards ; Models, Theoretical
    Language English
    Publishing date 2018-10-18
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1554-8937
    ISSN (online) 1554-8937
    DOI 10.1021/acschembio.8b00881
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Article ; Online: Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

    Cáceres, Elena L / Mew, Nicholas C / Keiser, Michael J

    Journal of chemical information and modeling

    2020  Volume 60, Issue 12, Page(s) 5957–5970

    Abstract: Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test ... ...

    Abstract Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the
    MeSH term(s) Machine Learning ; Neural Networks, Computer
    Language English
    Publishing date 2020-11-27
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.0c00565
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Article ; Online: Learning Molecular Representations for Medicinal Chemistry.

    Chuang, Kangway V / Gunsalus, Laura M / Keiser, Michael J

    Journal of medicinal chemistry

    2020  Volume 63, Issue 16, Page(s) 8705–8722

    Abstract: The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish ... ...

    Abstract The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish quantitative structure-activity and structure-property relationships for drug discovery. Now, advances in deep learning make it possible to efficiently and compactly
    MeSH term(s) Cheminformatics ; Chemistry, Pharmaceutical/methods ; Deep Learning ; Models, Molecular ; Molecular Structure ; Organic Chemicals/chemistry ; Quantitative Structure-Activity Relationship
    Chemical Substances Organic Chemicals
    Language English
    Publishing date 2020-05-15
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 218133-2
    ISSN 1520-4804 ; 0022-2623
    ISSN (online) 1520-4804
    ISSN 0022-2623
    DOI 10.1021/acs.jmedchem.0c00385
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.B 151: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MB 1: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  10. Book ; Online: Learning Melanocytic Cell Masks from Adjacent Stained Tissue

    Tada, Mikio / Lang, Ursula E. / Yeh, Iwei / Wei, Maria L. / Keiser, Michael J.

    2022  

    Abstract: Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need ... ...

    Abstract Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates pixel-level annotation. Gigapixel-level pathologist labeling, however, is impractical. Herein, we propose a means to train deep neural networks for melanocytic cell segmentation from hematoxylin and eosin (H&E) stained sections and paired immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of 0.64 despite imperfect ground-truth labels.

    Comment: Accepted at Medical Image Learning with Limited & Noisy Data Workshop, Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 006
    Publishing date 2022-10-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top