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  1. Article ; Online: BERTwalk for integrating gene networks to predict gene- to pathway-level properties.

    Nasser, Rami / Sharan, Roded

    Bioinformatics advances

    2023  Volume 3, Issue 1, Page(s) vbad086

    Abstract: Motivation: Graph representation learning is a fundamental problem in the field of data science with applications to integrative analysis of biological networks. Previous work in this domain was mostly limited to shallow representation techniques. A ... ...

    Abstract Motivation: Graph representation learning is a fundamental problem in the field of data science with applications to integrative analysis of biological networks. Previous work in this domain was mostly limited to shallow representation techniques. A recent deep representation technique, BIONIC, has achieved state-of-the-art results in a variety of tasks but used arbitrarily defined components.
    Results: Here, we present BERTwalk, an unsupervised learning scheme that combines the BERT masked language model with a network propagation regularization for graph representation learning. The transformation from networks to texts allows our method to naturally integrate different networks and provide features that inform not only nodes or edges but also pathway-level properties. We show that our BERTwalk model outperforms BIONIC, as well as four other recent methods, on two comprehensive benchmarks in yeast and human. We further show that our model can be utilized to infer functional pathways and their effects.
    Availability and implementation: Code and data are available at https://github.com/raminass/BERTwalk.
    Contact: roded@tauex.tau.ac.il.
    Language English
    Publishing date 2023-07-03
    Publishing country England
    Document type Journal Article
    ISSN 2635-0041
    ISSN (online) 2635-0041
    DOI 10.1093/bioadv/vbad086
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: From Leiden to Tel-Aviv University (TAU): exploring clustering solutions via a genetic algorithm.

    Gilad, Gal / Sharan, Roded

    PNAS nexus

    2023  Volume 2, Issue 6, Page(s) pgad180

    Abstract: Graph clustering is a fundamental problem in machine learning with numerous applications in data science. State-of-the-art approaches to the problem, Louvain and Leiden, aim at optimizing the modularity function. However, their greedy nature leads to ... ...

    Abstract Graph clustering is a fundamental problem in machine learning with numerous applications in data science. State-of-the-art approaches to the problem, Louvain and Leiden, aim at optimizing the modularity function. However, their greedy nature leads to fast convergence to sub-optimal solutions. Here, we design a new approach to graph clustering, Tel-Aviv University (TAU), that efficiently explores the solution space using a genetic algorithm. We benchmark TAU on synthetic and real data sets and show its superiority over previous methods both in terms of the modularity of the computed solution and its similarity to a ground-truth partition when such exists. TAU is available at https://github.com/GalGilad/TAU.
    Language English
    Publishing date 2023-06-01
    Publishing country England
    Document type Journal Article
    ISSN 2752-6542
    ISSN (online) 2752-6542
    DOI 10.1093/pnasnexus/pgad180
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A network-based method for associating genes with autism spectrum disorder.

    Zadok, Neta / Ast, Gil / Sharan, Roded

    Frontiers in bioinformatics

    2024  Volume 4, Page(s) 1295600

    Abstract: Autism spectrum disorder (ASD) is a highly heritable complex disease that affects 1% of the population, yet its underlying molecular mechanisms are largely unknown. Here we study the problem of predicting causal genes for ASD by combining genome-scale ... ...

    Abstract Autism spectrum disorder (ASD) is a highly heritable complex disease that affects 1% of the population, yet its underlying molecular mechanisms are largely unknown. Here we study the problem of predicting causal genes for ASD by combining genome-scale data with a network propagation approach. We construct a predictor that integrates multiple omic data sets that assess genomic, transcriptomic, proteomic, and phosphoproteomic associations with ASD. In cross validation our predictor yields mean area under the ROC curve of 0.87 and area under the precision-recall curve of 0.89. We further show that it outperforms previous gene-level predictors of autism association. Finally, we show that we can use the model to predict genes associated with Schizophrenia which is known to share genetic components with ASD.
    Language English
    Publishing date 2024-03-08
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2024.1295600
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Multi-task learning for predicting SARS-CoV-2 antibody escape.

    Gross, Barak / Sharan, Roded

    Frontiers in genetics

    2022  Volume 13, Page(s) 886649

    Abstract: The coronavirus pandemic has revolutionized our world, with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attack are variants that can evade the vaccine. Thus, a fundamental problem of growing ... ...

    Abstract The coronavirus pandemic has revolutionized our world, with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attack are variants that can evade the vaccine. Thus, a fundamental problem of growing importance is the identification of mutations of concern with high escape probability. In this paper we develop a computational framework that harnesses systematic mutation screens in the receptor binding domain of the viral Spike protein for escape prediction. The framework analyzes data on escape from multiple antibodies simultaneously, creating a latent representation of mutations that is shown to be effective in predicting escape and binding properties of the virus. We use this representation to validate the escape potential of current SARS-CoV-2 variants.
    Language English
    Publishing date 2022-08-11
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2022.886649
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Batch correction of single-cell sequencing data via an autoencoder architecture.

    Danino, Reut / Nachman, Iftach / Sharan, Roded

    Bioinformatics advances

    2023  Volume 4, Issue 1, Page(s) vbad186

    Abstract: Motivation: Technical differences between gene expression sequencing experiments can cause variations in the data in the form of batch effect biases. These do not represent true biological variations between samples and can lead to false conclusions or ... ...

    Abstract Motivation: Technical differences between gene expression sequencing experiments can cause variations in the data in the form of batch effect biases. These do not represent true biological variations between samples and can lead to false conclusions or hinder the ability to integrate multiple datasets. Since there is a growing need for the joint analysis of single
    Results: We developed a semi-supervised deep learning architecture called Autoencoder-based Batch Correction (ABC) for integrating single
    Language English
    Publishing date 2023-12-28
    Publishing country England
    Document type Journal Article
    ISSN 2635-0041
    ISSN (online) 2635-0041
    DOI 10.1093/bioadv/vbad186
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: A mutation-level covariate model for mutational signatures.

    Kahane, Itay / Leiserson, Mark D M / Sharan, Roded

    PLoS computational biology

    2023  Volume 19, Issue 6, Page(s) e1011195

    Abstract: Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential ... ...

    Abstract Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.
    MeSH term(s) Humans ; Neoplasms/genetics ; Mutation/genetics ; Genomics
    Language English
    Publishing date 2023-06-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011195
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: A Biterm Topic Model for Sparse Mutation Data.

    Sason, Itay / Chen, Yuexi / Leiserson, Mark D M / Sharan, Roded

    Cancers

    2023  Volume 15, Issue 5

    Abstract: Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole- ... ...

    Abstract Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole-exome sequencing. Methods that process sparse mutation data typically found in practice are only in the earliest stages of development. In particular, we previously developed the Mix model that clusters samples to handle data sparsity. However, the Mix model had two hyper-parameters, including the number of signatures and the number of clusters, that were very costly to learn. Therefore, we devised a new method that was several orders-of-magnitude more efficient for handling sparse data, was based on mutation co-occurrences, and imitated word co-occurrence analyses of Twitter texts. We showed that the model produced significantly improved hyper-parameter estimates that led to higher likelihoods of discovering overlooked data and had better correspondence with known signatures.
    Language English
    Publishing date 2023-03-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers15051601
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: A comparative analysis of telomere length maintenance circuits in fission and budding yeast.

    Peretz, Iftah / Kupiec, Martin / Sharan, Roded

    Frontiers in genetics

    2022  Volume 13, Page(s) 1033113

    Abstract: The natural ends of the linear eukaryotic chromosomes are protected by telomeres, which also play an important role in aging and cancer development. Telomere length varies between species, but it is strictly controlled in all organisms. The process of ... ...

    Abstract The natural ends of the linear eukaryotic chromosomes are protected by telomeres, which also play an important role in aging and cancer development. Telomere length varies between species, but it is strictly controlled in all organisms. The process of Telomere Length Maintenance (TLM) involves many pathways, protein complexes and interactions that were first discovered in budding and fission yeast model organisms (
    Language English
    Publishing date 2022-11-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2022.1033113
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

    Nasser, Rami / Eldar, Yonina C / Sharan, Roded

    Journal of computational biology : a journal of computational molecular cell biology

    2022  Volume 29, Issue 1, Page(s) 45–55

    Abstract: Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possible, ...

    Abstract Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possible, there are various iterative algorithms for NMF optimization that converge to locally optimal solutions. Such techniques can also serve as a starting point for deep learning methods that unroll the algorithmic iterations into layers of a deep network. In this study, we develop unfolded deep networks for NMF and several regularized variants in both a supervised and an unsupervised setting. We apply our method to various mutation data sets to reconstruct their underlying mutational signatures and their exposures. We demonstrate the increased accuracy of our approach over standard formulations in analyzing simulated and real mutation data.
    MeSH term(s) Algorithms ; Breast Neoplasms/genetics ; Computational Biology ; Computer Simulation ; DNA Mutational Analysis/statistics & numerical data ; Databases, Genetic/statistics & numerical data ; Deep Learning ; Female ; Humans ; Mutation ; Neural Networks, Computer ; Supervised Machine Learning ; Unsupervised Machine Learning
    Language English
    Publishing date 2022-01-05
    Publishing country United States
    Document type Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2030900-4
    ISSN 1557-8666 ; 1066-5277
    ISSN (online) 1557-8666
    ISSN 1066-5277
    DOI 10.1089/cmb.2021.0438
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online ; Conference proceedings: Introduction. These two issues contain a selected subset of the papers presented at RECOMB 2014.

    Sharan, Roded

    Journal of computational biology : a journal of computational molecular cell biology

    2015  Volume 22, Issue 5, Page(s) 335

    MeSH term(s) Computational Biology/instrumentation ; Computational Biology/methods ; Computational Biology/trends ; Humans
    Language English
    Publishing date 2015-05
    Publishing country United States
    Document type Congresses ; Editorial
    ZDB-ID 2030900-4
    ISSN 1557-8666 ; 1066-5277
    ISSN (online) 1557-8666
    ISSN 1066-5277
    DOI 10.1089/cmb.2015.030P
    Database MEDical Literature Analysis and Retrieval System OnLINE

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