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  1. Article ; Online: Symbolic expression generation

    Popov, Sergei / Lazarev, Mikhail / Belavin, Vladislav / Derkach, Denis / Ustyuzhanin, Andrey

    PeerJ. Computer science

    2023  Volume 9, Page(s) e1241

    Abstract: There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions. Meanwhile, ... ...

    Abstract There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation
    Language English
    Publishing date 2023-03-07
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.1241
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  2. Article ; Online: NFAD: fixing anomaly detection using normalizing flows.

    Ryzhikov, Artem / Borisyak, Maxim / Ustyuzhanin, Andrey / Derkach, Denis

    PeerJ. Computer science

    2021  Volume 7, Page(s) e757

    Abstract: Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional ... ...

    Abstract Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods,
    Language English
    Publishing date 2021-11-18
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.757
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Symbolic expression generation via Variational Auto-Encoder

    Popov, Sergei / Lazarev, Mikhail / Belavin, Vladislav / Derkach, Denis / Ustyuzhanin, Andrey

    2023  

    Abstract: There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Deep Neural Networks do not provide interpretable solutions. Meanwhile, ... ...

    Abstract There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Deep Neural Networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). In a nutshell, we suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Symbolic Computation
    Subject code 006
    Publishing date 2023-01-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Latent Neural Stochastic Differential Equations for Change Point Detection

    Ryzhikov, Artem / Hushchyn, Mikhail / Derkach, Denis

    2022  

    Abstract: Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point ... ...

    Abstract Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.
    Keywords Computer Science - Machine Learning
    Publishing date 2022-08-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Platelet factor 4 improves survival in a murine model of antibiotic-susceptible and methicillin-resistant

    Podolnikova, Nataly P / Lishko, Valeryi K / Roberson, Robert / Koh, Zhiqian / Derkach, Dmitry / Richardson, David / Sheller, Michael / Ugarova, Tatiana P

    Frontiers in cellular and infection microbiology

    2023  Volume 13, Page(s) 1217103

    Abstract: The complement receptor CR3, also known as integrin Mac-1 (CD11b/CD18), is one of the major phagocytic receptors on the surface of neutrophils and macrophages. We previously demonstrated that in its protein ligands, Mac-1 binds sequences enriched in ... ...

    Abstract The complement receptor CR3, also known as integrin Mac-1 (CD11b/CD18), is one of the major phagocytic receptors on the surface of neutrophils and macrophages. We previously demonstrated that in its protein ligands, Mac-1 binds sequences enriched in basic and hydrophobic residues and strongly disfavors negatively charged sequences. The avoidance by Mac-1 of negatively charged surfaces suggests that the bacterial wall and bacterial capsule possessing net negative electrostatic charge may repel Mac-1 and that the cationic Mac-1 ligands can overcome this evasion by acting as opsonins. Indeed, we previously showed that opsonization of Gram-negative
    MeSH term(s) Animals ; Mice ; Methicillin-Resistant Staphylococcus aureus ; Anti-Bacterial Agents/pharmacology ; Platelet Factor 4/chemistry ; Platelet Factor 4/metabolism ; Staphylococcus aureus/metabolism ; Disease Models, Animal ; Phagocytosis ; Macrophage-1 Antigen/metabolism ; Immunologic Factors ; Peritonitis/drug therapy
    Chemical Substances Anti-Bacterial Agents ; Platelet Factor 4 (37270-94-3) ; Macrophage-1 Antigen ; Immunologic Factors
    Language English
    Publishing date 2023-10-04
    Publishing country Switzerland
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2619676-1
    ISSN 2235-2988 ; 2235-2988
    ISSN (online) 2235-2988
    ISSN 2235-2988
    DOI 10.3389/fcimb.2023.1217103
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: PLATELET FACTOR 4 (PF4) IMPROVES SURVIVAL IN A MURINE MODEL OF ANTIBIOTIC-SUSCEPTIBLE AND METHICILLIN-RESISTANT

    Podolnikova, Nataly P / Lishko, Valeryi K / Roberson, Robert / Koh, Zhqian / Derkach, Dmitry / Richardson, David / Sheller, Michael / Ugarova, Tatiana P

    bioRxiv : the preprint server for biology

    2023  

    Abstract: The complement receptor CR3, also known as integrin Mac-1 (CD11b/CD18), is one of the major phagocytic receptors on the surface of neutrophils and macrophages. We previously demonstrated that in its protein ligands, Mac-1 binds sequences enriched in ... ...

    Abstract The complement receptor CR3, also known as integrin Mac-1 (CD11b/CD18), is one of the major phagocytic receptors on the surface of neutrophils and macrophages. We previously demonstrated that in its protein ligands, Mac-1 binds sequences enriched in basic and hydrophobic residues and strongly disfavors negatively charged sequences. The avoidance by Mac-1 of negatively charged surfaces suggests that the bacterial wall and bacterial capsule possessing net negative electrostatic charge may repel Mac-1 and that the cationic Mac-1 ligands can overcome this evasion by acting as opsonins. Indeed, we previously showed that opsonization of Gram-negative
    Language English
    Publishing date 2023-08-26
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.08.25.554865
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Metformin pretreatment rescues olfactory memory associated with subependymal zone neurogenesis in a juvenile model of cranial irradiation.

    Derkach, Daniel / Kehtari, Tarlan / Renaud, Matthew / Heidari, Mohsen / Lakshman, Nishanth / Morshead, Cindi M

    Cell reports. Medicine

    2021  Volume 2, Issue 4, Page(s) 100231

    Abstract: Cranial irradiation (IR) is an effective adjuvant therapy in the treatment of childhood brain tumors but results in long-lasting cognitive deficits associated with impaired neurogenesis, as evidenced in rodent models. Metformin has been shown to expand ... ...

    Abstract Cranial irradiation (IR) is an effective adjuvant therapy in the treatment of childhood brain tumors but results in long-lasting cognitive deficits associated with impaired neurogenesis, as evidenced in rodent models. Metformin has been shown to expand the endogenous neural stem cell (NSC) pool and promote neurogenesis under physiological conditions and in response to neonatal brain injury, suggesting a potential role in neurorepair. Here, we assess whether metformin pretreatment, a clinically feasible treatment for children receiving cranial IR, promotes neurorepair in a mouse cranial IR model. Using immunofluorescence and the
    MeSH term(s) Animals ; Brain/drug effects ; Brain/pathology ; Brain Injuries/drug therapy ; Brain Injuries/pathology ; Cognitive Dysfunction/drug therapy ; Cognitive Dysfunction/pathology ; Cranial Irradiation/methods ; Disease Models, Animal ; Male ; Memory, Long-Term/drug effects ; Metformin/administration & dosage ; Metformin/pharmacology ; Mice, Inbred C57BL ; Neural Stem Cells/drug effects ; Neural Stem Cells/pathology ; Neurogenesis/drug effects ; Mice
    Chemical Substances Metformin (9100L32L2N)
    Language English
    Publishing date 2021-04-06
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2666-3791
    ISSN (online) 2666-3791
    DOI 10.1016/j.xcrm.2021.100231
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  8. Book ; Online: Using machine learning to speed up new and upgrade detector studies

    Ratnikov, F. / Derkach, D. / Boldyrev, A. / Shevelev, A. / Fakanov, P. / Matyushin, L.

    a calorimeter case

    2020  

    Abstract: In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design ... ...

    Abstract In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may speed up the verification of the possible detector configurations and will automate the entire detector R\&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R\&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The spatial reconstruction and time of arrival properties for the electromagnetic calorimeter were demonstrated.

    Comment: Talk presented on CHEP 2019 conference
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Computational Physics
    Subject code 670
    Publishing date 2020-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Cranial irradiation in juvenile mice leads to early and sustained defects in the stem and progenitor cell pools and late cognitive impairments.

    Ruddy, Rebecca M / Derkach, Daniel / Dadwal, Parvati / Morshead, Cindi M

    Brain research

    2019  Volume 1727, Page(s) 146548

    Abstract: Cranial irradiation is used in combination with other therapies as a treatment for brain tumours and is thought to contribute to long-term cognitive deficits. Several rodent models have demonstrated that these cognitive deficits may be correlated with ... ...

    Abstract Cranial irradiation is used in combination with other therapies as a treatment for brain tumours and is thought to contribute to long-term cognitive deficits. Several rodent models have demonstrated that these cognitive deficits may be correlated with damage to neural progenitor cells in the subventricular zone (SVZ) and dentate gyrus (DG), the two neurogenic niches of the brain. Studies in rodent models typically assess the proliferating progenitor population, but rarely investigate the effect of cranial irradiation on the neural stem cell pool. Further, few studies evaluate the effects in juveniles, an age when children typically receive this treatment. Herein, we examine the cellular and behavioural effects of juvenile cranial irradiation on stem and progenitor populations in the two neurogenic regions of the brain and assess cognitive outcomes. We found regionally distinct effects of cranial irradiation in the juvenile brain. In the SVZ, we observed a defect in the stem cell pool and a concomitant decrease in proliferating cells that were maintained for at least one week. In the DG, a similar defect in the stem cell pool and proliferating cells was observed and persisted in the stem cell population. Finally, we demonstrated that cranial irradiation resulted in late cognitive deficits. This study demonstrates that juvenile cranial irradiation leads to regionally distinct defects in the stem and progenitor populations, and late cognitive deficits, which may be important factors in determining therapeutic targets and timing of interventions following cranial irradiation.
    MeSH term(s) Animals ; Cognitive Dysfunction/etiology ; Cranial Irradiation ; Dentate Gyrus/pathology ; Dentate Gyrus/radiation effects ; Lateral Ventricles/pathology ; Lateral Ventricles/radiation effects ; Memory/radiation effects ; Mice, Inbred C57BL ; Neural Stem Cells/pathology ; Neural Stem Cells/radiation effects ; Stem Cell Niche/radiation effects ; Stem Cells/pathology ; Stem Cells/radiation effects
    Language English
    Publishing date 2019-11-09
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1200-2
    ISSN 1872-6240 ; 0006-8993
    ISSN (online) 1872-6240
    ISSN 0006-8993
    DOI 10.1016/j.brainres.2019.146548
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  10. Book ; Online: Online Neural Networks for Change-Point Detection

    Hushchyn, Mikhail / Arzymatov, Kenenbek / Derkach, Denis

    2020  

    Abstract: Moments when a time series changes its behaviour are called change points. Detection of such points is a well-known problem, which can be found in many applications: quality monitoring of industrial processes, failure detection in complex systems, health ...

    Abstract Moments when a time series changes its behaviour are called change points. Detection of such points is a well-known problem, which can be found in many applications: quality monitoring of industrial processes, failure detection in complex systems, health monitoring, speech recognition and video analysis. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two online change-point detection approaches based on neural networks. These algorithms demonstrate linear computational complexity and are suitable for change-point detection in large time series. We compare them with the best known algorithms on various synthetic and real world data sets. Experiments show that the proposed methods outperform known approaches.

    Comment: 24 pages, 8 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-10-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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