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  1. Article ; Online: Building a two-way street between cell biology and machine learning.

    Uhler, Caroline

    Nature cell biology

    2024  Volume 26, Issue 1, Page(s) 13–14

    MeSH term(s) Machine Learning ; Cell Biology
    Language English
    Publishing date 2024-01-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 1474722-4
    ISSN 1476-4679 ; 1465-7392
    ISSN (online) 1476-4679
    ISSN 1465-7392
    DOI 10.1038/s41556-023-01279-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5169: Show issues Location:
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  2. Article: Causal Structure Learning: A Combinatorial Perspective.

    Squires, Chandler / Uhler, Caroline

    Foundations of computational mathematics (New York, N.Y.)

    2022  , Page(s) 1–35

    Abstract: In this review, we discuss approaches for learning causal structure from data, also ... ...

    Abstract In this review, we discuss approaches for learning causal structure from data, also called
    Language English
    Publishing date 2022-08-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2035475-7
    ISSN 1615-3383 ; 1615-3375
    ISSN (online) 1615-3383
    ISSN 1615-3375
    DOI 10.1007/s10208-022-09581-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Removing Biases from Molecular Representations via Information Maximization.

    Wang, Chenyu / Gupta, Sharut / Uhler, Caroline / Jaakkola, Tommi

    ArXiv

    2023  

    Abstract: High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a ... ...

    Abstract High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. It adaptively reweighs samples to equalize their implied batch distribution. Extensive experiments on drug screening data reveal InfoCORE's superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes. The code is available at https://github.com/uhlerlab/InfoCORE.
    Language English
    Publishing date 2023-12-01
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Adhesome Receptor Clustering is Accompanied by the Colocalization of the Associated Genes in the Cell Nucleus.

    Cammarata, Louis V / Uhler, Caroline / Shivashankar, G V

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Proteins on the cell membrane cluster to respond to extracellular signals; for example, adhesion proteins cluster to enhance extracellular matrix sensing; or T-cell receptors cluster to enhance antigen sensing. Importantly, the maturation of such ... ...

    Abstract Proteins on the cell membrane cluster to respond to extracellular signals; for example, adhesion proteins cluster to enhance extracellular matrix sensing; or T-cell receptors cluster to enhance antigen sensing. Importantly, the maturation of such receptor clusters requires transcriptional control to adapt and reinforce the extracellular signal sensing. However, it has been unclear how such efficient clustering mechanisms are encoded at the level of the genes that code for these receptor proteins. Using the adhesome as an example, we show that genes that code for adhesome receptor proteins are spatially co-localized and co-regulated within the cell nucleus. Towards this, we use Hi-C maps combined with RNA-seq data of adherent cells to map the correspondence between adhesome receptor proteins and their associated genes. Interestingly, we find that the transcription factors that regulate these genes are also co-localized with the adhesome gene loci, thereby potentially facilitating a transcriptional reinforcement of the extracellular matrix sensing machinery. Collectively, our results highlight an important layer of transcriptional control of cellular signal sensing.
    Language English
    Publishing date 2023-12-08
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.12.07.570697
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Synthetic Lethality Screening with Recursive Feature Machines.

    Cai, Cathy / Radhakrishnan, Adityanarayanan / Uhler, Caroline

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic ... ...

    Abstract Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic vulnerabilities. The rise of large-scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPR gene knockout from cell line embeddings consisting of gene expression and mutation features. After training, RFMs use a statistical operator known as average gradient outer product to provide weights for each feature indicating the importance of each feature in predicting cellular viability. We subsequently apply correlation-based filters to re-weight RFM feature importances and identify those features that are most indicative of low cellular viability. Our resulting pipeline is computationally efficient, taking under 3 minutes for analyzing all 17, 453 knockouts from DepMap for candidate SL pairs. We show that our pipeline more accurately recovers experimentally verified SL pairs than prior approaches. Moreover, our pipeline finds new candidate SL pairs, thereby opening novel avenues for identifying genetic vulnerabilities in cancer.
    Language English
    Publishing date 2023-12-05
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.12.03.569803
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Wide and deep neural networks achieve consistency for classification.

    Radhakrishnan, Adityanarayanan / Belkin, Mikhail / Uhler, Caroline

    Proceedings of the National Academy of Sciences of the United States of America

    2023  Volume 120, Issue 14, Page(s) e2208779120

    Abstract: While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standard procedures are consistent for classification, i.e., whether such models ...

    Abstract While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standard procedures are consistent for classification, i.e., whether such models minimize the probability of misclassification for arbitrary data distributions. In this work, we identify and construct an explicit set of neural network classifiers that are consistent. Since effective neural networks in practice are typically both wide and deep, we analyze infinitely wide networks that are also infinitely deep. In particular, using the recent connection between infinitely wide neural networks and neural tangent kernels, we provide explicit activation functions that can be used to construct networks that achieve consistency. Interestingly, these activation functions are simple and easy to implement, yet differ from commonly used activations such as ReLU or sigmoid. More generally, we create a taxonomy of infinitely wide and deep networks and show that these models implement one of three well-known classifiers depending on the activation function used: 1) 1-nearest neighbor (model predictions are given by the label of the nearest training example); 2) majority vote (model predictions are given by the label of the class with the greatest representation in the training set); or 3) singular kernel classifiers (a set of classifiers containing those that achieve consistency). Our results highlight the benefit of using deep networks for classification tasks, in contrast to regression tasks, where excessive depth is harmful.
    MeSH term(s) Neural Networks, Computer ; Machine Learning
    Language English
    Publishing date 2023-03-30
    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 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2208779120
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1148: Show issues Location:
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  7. Article ; Online: Transfer Learning with Kernel Methods.

    Radhakrishnan, Adityanarayanan / Ruiz Luyten, Max / Prasad, Neha / Uhler, Caroline

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 5570

    Abstract: Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop ... ...

    Abstract Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws.
    Language English
    Publishing date 2023-09-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-41215-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation.

    Jin, Wengong / Sarkizova, Siranush / Chen, Xun / Hacohen, Nir / Uhler, Caroline

    ArXiv

    2023  

    Abstract: Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery. Prior work focused on supervised learning methods using a large set of binding affinity data for small molecules, but it is hard to apply the same strategy to other ... ...

    Abstract Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery. Prior work focused on supervised learning methods using a large set of binding affinity data for small molecules, but it is hard to apply the same strategy to other drug classes like antibodies as labelled data is limited. In this paper, we explore unsupervised approaches and reformulate binding energy prediction as a generative modeling task. Specifically, we train an energy-based model on a set of unlabelled protein-ligand complexes using SE(3) denoising score matching and interpret its log-likelihood as binding affinity. Our key contribution is a new equivariant rotation prediction network called Neural Euler's Rotation Equations (NERE) for SE(3) score matching. It predicts a rotation by modeling the force and torque between protein and ligand atoms, where the force is defined as the gradient of an energy function with respect to atom coordinates. We evaluate NERE on protein-ligand and antibody-antigen binding affinity prediction benchmarks. Our model outperforms all unsupervised baselines (physics-based and statistical potentials) and matches supervised learning methods in the antibody case.
    Language English
    Publishing date 2023-12-12
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Simple, fast, and flexible framework for matrix completion with infinite width neural networks.

    Radhakrishnan, Adityanarayanan / Stefanakis, George / Belkin, Mikhail / Uhler, Caroline

    Proceedings of the National Academy of Sciences of the United States of America

    2022  Volume 119, Issue 16, Page(s) e2115064119

    Abstract: Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. ... ...

    Abstract Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Remarkably, taking the width of a neural network to infinity allows for improved computational performance. In this work, we develop an infinite width neural network framework for matrix completion that is simple, fast, and flexible. Simplicity and speed come from the connection between the infinite width limit of neural networks and kernels known as neural tangent kernels (NTK). In particular, we derive the NTK for fully connected and convolutional neural networks for matrix completion. The flexibility stems from a feature prior, which allows encoding relationships between coordinates of the target matrix, akin to semisupervised learning. The effectiveness of our framework is demonstrated through competitive results for virtual drug screening and image inpainting/reconstruction. We also provide an implementation in Python to make our framework accessible on standard hardware to a broad audience.
    MeSH term(s) Computers ; Image Processing, Computer-Assisted/methods ; Machine Learning ; Neural Networks, Computer ; Supervised Machine Learning
    Language English
    Publishing date 2022-04-11
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2115064119
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1148: Show issues Location:
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  10. Article ; Online: Mechano-genomic regulation of coronaviruses and its interplay with ageing.

    Uhler, Caroline / Shivashankar, G V

    Nature reviews. Molecular cell biology

    2020  Volume 21, Issue 5, Page(s) 247–248

    MeSH term(s) Aging/physiology ; Betacoronavirus/pathogenicity ; Betacoronavirus/physiology ; COVID-19 ; Coronavirus Infections/pathology ; Cytoskeleton/metabolism ; Cytoskeleton/virology ; Genome, Viral ; Host-Pathogen Interactions/physiology ; Humans ; NF-kappa B/metabolism ; Pandemics ; Pneumonia, Viral/pathology ; SARS-CoV-2 ; Signal Transduction ; Virus Replication/physiology
    Chemical Substances NF-kappa B
    Keywords covid19
    Language English
    Publishing date 2020-03-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2031313-5
    ISSN 1471-0080 ; 1471-0072
    ISSN (online) 1471-0080
    ISSN 1471-0072
    DOI 10.1038/s41580-020-0242-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.MG 26: Show issues

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