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  1. Article ; Online: Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit.

    Gussow, Ayal B / Koonin, Eugene V / Auslander, Noam

    NAR cancer

    2021  Volume 3, Issue 2, Page(s) zcab017

    Abstract: Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict ... ...

    Abstract Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict patient prognosis. Here we present CLICnet, a method that utilizes mutational data to cluster patients by survival rate. CLICnet employs Restricted Boltzmann Machines, a type of generative neural network, which allows for the capture of complex mutational patterns associated with patient survival in different cancer types. For some cancer types, clustering produced by CLICnet also predicts benefit from anti-PD1 immune checkpoint blockade therapy, whereas for other cancer types, the mutational processes associated with survival are different from those associated with the improved anti-PD1 survival benefit. Thus, CLICnet has the ability to systematically identify and catalogue combinations of mutations that predict cancer survival, unveiling intricate associations between mutations, survival, and immunotherapy benefit.
    Language English
    Publishing date 2021-05-17
    Publishing country England
    Document type Journal Article
    ISSN 2632-8674
    ISSN (online) 2632-8674
    DOI 10.1093/narcan/zcab017
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  2. Article ; Online: Incorporating Machine Learning into Established Bioinformatics Frameworks.

    Auslander, Noam / Gussow, Ayal B / Koonin, Eugene V

    International journal of molecular sciences

    2021  Volume 22, Issue 6

    Abstract: The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and ... ...

    Abstract The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.
    MeSH term(s) Algorithms ; Computational Biology/trends ; Databases, Factual/trends ; Humans ; Machine Learning/trends ; Systems Biology/trends
    Language English
    Publishing date 2021-03-12
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms22062903
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Incorporating Machine Learning into Established Bioinformatics Frameworks

    Noam Auslander / Ayal B. Gussow / Eugene V. Koonin

    International Journal of Molecular Sciences, Vol 22, Iss 2903, p

    2021  Volume 2903

    Abstract: The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and ... ...

    Abstract The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.
    Keywords machine learning ; deep learning ; bioinformatics methods ; phylogenetics ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Prediction of the incubation period for COVID-19 and future virus disease outbreaks

    Gussow B Ayal, Auslander Noam, Wolf I Yuri, Koonin V Eugene

    2020  

    Abstract: Background: A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 ... ...

    Abstract Background: A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well-being and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood. Results: We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC-content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks. Conclusions: Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connection between these features and disease progression can be expected to reveal key aspects of virus pathogenesis
    Keywords covid19
    Subject code 612
    Publishing date 2020-11-03
    Publishing country eu
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

    Ayal B. Gussow, Noam Auslander, Guilhem Faure, Yuri I. Wolf, Feng Zhang, Eugene V. Koonin

    2020  

    Abstract: Dataset S2. Complete nucleotide sequence alignment of all CoV (of human and non-human hosts). ...

    Abstract Dataset S2. Complete nucleotide sequence alignment of all CoV (of human and non-human hosts).
    Keywords covid19
    Publishing date 2020-05-18
    Publishing country eu
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

    Ayal B. Gussow, Noam Auslander, Guilhem Faure, Yuri I. Wolf, Feng Zhang, Eugene V. Koonin

    2020  

    Abstract: Dataset S1.Complete nucleotide sequence alignment of all human CoV used for region identification. Dataset S2.Complete nucleotide sequence alignment of all CoV (of human and non-human hosts). Dataset S3.Distances between leaves (each CoV strain in ... ...

    Abstract Dataset S1.Complete nucleotide sequence alignment of all human CoV used for region identification. Dataset S2.Complete nucleotide sequence alignment of all CoV (of human and non-human hosts). Dataset S3.Distances between leaves (each CoV strain in Dataset S2 was considered), from every reference genome of each of the seven human CoV. Dataset S4.Alignment of strains used for zoonotic jump analysis.
    Keywords covid19
    Publishing date 2020-05-18
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Prediction of the incubation period for COVID-19 and future virus disease outbreaks.

    Gussow, Ayal B / Auslander, Noam / Wolf, Yuri I / Koonin, Eugene V

    BMC biology

    2020  Volume 18, Issue 1, Page(s) 186

    Abstract: Background: A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 ... ...

    Abstract Background: A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well-being, and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood.
    Results: We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks.
    Conclusions: The length of the incubation period in viral diseases strongly correlates with disease severity, emphasizing the biological and epidemiological importance of the incubation period. Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connections between these features and disease progression can be expected to reveal key aspects of virus pathogenesis.
    MeSH term(s) COVID-19/pathology ; COVID-19/virology ; Computer Simulation ; Genome, Viral ; Humans ; Infectious Disease Incubation Period ; Models, Biological ; Mutation ; Quarantine ; SARS-CoV-2/genetics
    Language English
    Publishing date 2020-11-30
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Intramural
    ISSN 1741-7007
    ISSN (online) 1741-7007
    DOI 10.1186/s12915-020-00919-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

    Dimitrios Vitsios / Ryan S. Dhindsa / Lawrence Middleton / Ayal B. Gussow / Slavé Petrovski

    Nature Communications, Vol 12, Iss 1, Pp 1-

    2021  Volume 14

    Abstract: Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific ... ...

    Abstract Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.
    Keywords Science ; Q
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.

    Vitsios, Dimitrios / Dhindsa, Ryan S / Middleton, Lawrence / Gussow, Ayal B / Petrovski, Slavé

    Nature communications

    2021  Volume 12, Issue 1, Page(s) 1504

    Abstract: Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate ... ...

    Abstract Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome.
    MeSH term(s) DNA, Intergenic ; Deep Learning ; Genetic Variation ; Genome, Human ; Genomics ; Humans ; Sequence Analysis, DNA ; Whole Genome Sequencing
    Chemical Substances DNA, Intergenic
    Language English
    Publishing date 2021-03-08
    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/s41467-021-21790-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.

    Gussow, Ayal B / Auslander, Noam / Faure, Guilhem / Wolf, Yuri I / Zhang, Feng / Koonin, Eugene V

    bioRxiv : the preprint server for biology

    2020  

    Abstract: SARS-CoV-2 poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human ...

    Abstract SARS-CoV-2 poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and MERS-CoV, from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial elements of coronavirus pathogenicity and possible targets for diagnostics, prognostication and interventions.
    Keywords covid19
    Language English
    Publishing date 2020-04-09
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2020.04.05.026450
    Database MEDical Literature Analysis and Retrieval System OnLINE

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