The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform

Shao Li - One of the best experts on this subject based on the ideXlab platform.

  • A Network Target-based Approach for Evaluating Multicomponent Synergy
    2020
    Co-Authors: Shao Li, Ningbo Zhang, Bo Zhang
    Abstract:

    Evaluation of multicomponent synergy is a critical point in current drug combination studies. However, it is still an ongoing challenge to prioritize the synergistic combination from various pharmacological agents in a high throughput manner. Here we proposed a Network Target-based approach termed NIMS (Network Target-based Identification of Multicomponent Synergy), and showed that NIMS can not only recover the agent pairs with known synergistic effects, but also successfully predict synergistic agents from anti-angiogenic traditional Chinese medicine.

  • Network Target for screening synergistic drug combinations with application to traditional Chinese medicine
    BMC Systems Biology, 2020
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Abstract Background Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. Results In this work, we proposed for the first time a “Network Target”-based paradigm instead of the traditional "single Target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network Target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological Network as a therapeutic Target and assumes that the relationship among agents can be transferred to Network interactions among the molecular level entities (Targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background Networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a Network Target perspective. Conclusions NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The Network Target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

  • Matrine Is Identified as a Novel Macropinocytosis Inducer by a Network Target Approach.
    Frontiers in Pharmacology, 2018
    Co-Authors: Bo Zhang, Xin Wang, Yan Li, Min Wu, Shu-yan Wang, Shao Li
    Abstract:

    Comprehensively understanding pharmacological functions of natural products is a key issue to be addressed for the discovery of new drugs. Unlike some single-Target drugs, nature products always exert diverse therapeutic effects through acting on a “Network” that consists of multiple Targets, making it necessary to develop a systematic approach, e.g. Network pharmacology, to reveal pharmacological functions of natural products and infer their mechanism of action. In this work, to identify the “Network Target” of a natural product, we perform a functional analysis of matrine, a marketed drug in China extracted from a medical herb Ku-Shen (Radix Sophorae Flavescentis). Here, the Network Target of matrine was firstly predicted by drugCIPHER, a genome-wide Target prediction method. Based on the Network Target of matrine, we performed a functional gene set enrichment analysis to computationally identify the potential pharmacological functions of matrine, most of which are supported by the literature evidence, including neurotoxicity and neuropharmacological activities of matrine. Furthermore, computational results demonstrated that matrine has a potential activity for the induction of macropinocytosis and the regulation of ATP metabolism. Our experimental data revealed that the large vesicles induced by matrine are consistent with the typical characteristics of macropinosome. Our verification results also suggested that matrine could decrease cellular ATP level. These findings demonstrated the availability and effectiveness of the Network Target strategy for identifying the comprehensive pharmacological functions of natural products.

  • Exploring traditional chinese medicine by a novel therapeutic concept of Network Target
    Chinese Journal of Integrative Medicine, 2016
    Co-Authors: Shao Li
    Abstract:

    Traditional Chinese medicine (TCM) holds a holistic theory, and specializes in balancing disordered human body using numerous natural products, particularly Chinese herbal formulae. TCM has certain treatment advantages for patients suffering from various complex diseases. However, due to the complex nature of TCM, it remains difficult to unveil such holistic medicine by the current reductionism research strategies, which treat both herbal ingredients and Targets in isolation. Recently, an emerging Network pharmacology approach has been introduced to tackle this bottleneck problem. A TCM-derived novel therapeutic concept, "Network Target", which is different from the Western medicine's "oneTarget" concept, has been proposed from China. The Network Target strategy is able to illustrate the complex interactions among the biological systems, drugs, and complex diseases from a Network perspective, and thus provides an innovative approach to access ancient remedies in a precision manner and at a systematic level, which also highlights TCM's potential in current medical systems.

  • Network Target: a starting point for traditional Chinese medicine Network pharmacology
    China journal of Chinese materia medica, 2011
    Co-Authors: Shao Li
    Abstract:

    : Understanding the interactions between numerous chemical compounds of herbs or herbal formulae and complex biological systems related with diseases or traditional Chinese medicine (TCM) syndromes is one of great dilemmas in current studies on TCM. To address such a difficult issue, we propose a novel concept and methodology of "Network Target" based on our previous works and from the perspective of Network pharmacology as well as systems biology. The Network Target treats a disease-specific biomolecular Network as a Target to help design and predict the best possible treatments. Focused on mapping disease phenotypes and herbal compounds into biomolecular Networks and then calculating, analyzing and predicting the mechanism of their mutual interactions, the Network Target approaches will facilitate discovery of effective compounds and their combinations, elucidation of mechanistic relationships between herbal formulae and diseases or TCM syndromes, and development of rational drug designs for TCM. In this paper, our recent progresses on the methodology of Network Target and its applications in herbal medicine are reviewed to provide reference for the coming TCM Network pharmacology.

Ningbo Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A Network Target-based Approach for Evaluating Multicomponent Synergy
    2020
    Co-Authors: Shao Li, Ningbo Zhang, Bo Zhang
    Abstract:

    Evaluation of multicomponent synergy is a critical point in current drug combination studies. However, it is still an ongoing challenge to prioritize the synergistic combination from various pharmacological agents in a high throughput manner. Here we proposed a Network Target-based approach termed NIMS (Network Target-based Identification of Multicomponent Synergy), and showed that NIMS can not only recover the agent pairs with known synergistic effects, but also successfully predict synergistic agents from anti-angiogenic traditional Chinese medicine.

  • Network Target for screening synergistic drug combinations with application to traditional Chinese medicine
    BMC Systems Biology, 2020
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Abstract Background Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. Results In this work, we proposed for the first time a “Network Target”-based paradigm instead of the traditional "single Target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network Target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological Network as a therapeutic Target and assumes that the relationship among agents can be transferred to Network interactions among the molecular level entities (Targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background Networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a Network Target perspective. Conclusions NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The Network Target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

  • Network Target for screening synergistic drug combinations with application to traditional Chinese medicine.
    BMC systems biology, 2011
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. In this work, we proposed for the first time a "Network Target"-based paradigm instead of the traditional "single Target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network Target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological Network as a therapeutic Target and assumes that the relationship among agents can be transferred to Network interactions among the molecular level entities (Targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background Networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a Network Target perspective. NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The Network Target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

  • Network Target for screening synergistic drug combinations with application to traditional chinese medicine
    BMC Systems Biology, 2011
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Background Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.

Bo Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A Network Target-based Approach for Evaluating Multicomponent Synergy
    2020
    Co-Authors: Shao Li, Ningbo Zhang, Bo Zhang
    Abstract:

    Evaluation of multicomponent synergy is a critical point in current drug combination studies. However, it is still an ongoing challenge to prioritize the synergistic combination from various pharmacological agents in a high throughput manner. Here we proposed a Network Target-based approach termed NIMS (Network Target-based Identification of Multicomponent Synergy), and showed that NIMS can not only recover the agent pairs with known synergistic effects, but also successfully predict synergistic agents from anti-angiogenic traditional Chinese medicine.

  • Network Target for screening synergistic drug combinations with application to traditional Chinese medicine
    BMC Systems Biology, 2020
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Abstract Background Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. Results In this work, we proposed for the first time a “Network Target”-based paradigm instead of the traditional "single Target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network Target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological Network as a therapeutic Target and assumes that the relationship among agents can be transferred to Network interactions among the molecular level entities (Targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background Networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a Network Target perspective. Conclusions NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The Network Target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

  • Matrine Is Identified as a Novel Macropinocytosis Inducer by a Network Target Approach.
    Frontiers in Pharmacology, 2018
    Co-Authors: Bo Zhang, Xin Wang, Yan Li, Min Wu, Shu-yan Wang, Shao Li
    Abstract:

    Comprehensively understanding pharmacological functions of natural products is a key issue to be addressed for the discovery of new drugs. Unlike some single-Target drugs, nature products always exert diverse therapeutic effects through acting on a “Network” that consists of multiple Targets, making it necessary to develop a systematic approach, e.g. Network pharmacology, to reveal pharmacological functions of natural products and infer their mechanism of action. In this work, to identify the “Network Target” of a natural product, we perform a functional analysis of matrine, a marketed drug in China extracted from a medical herb Ku-Shen (Radix Sophorae Flavescentis). Here, the Network Target of matrine was firstly predicted by drugCIPHER, a genome-wide Target prediction method. Based on the Network Target of matrine, we performed a functional gene set enrichment analysis to computationally identify the potential pharmacological functions of matrine, most of which are supported by the literature evidence, including neurotoxicity and neuropharmacological activities of matrine. Furthermore, computational results demonstrated that matrine has a potential activity for the induction of macropinocytosis and the regulation of ATP metabolism. Our experimental data revealed that the large vesicles induced by matrine are consistent with the typical characteristics of macropinosome. Our verification results also suggested that matrine could decrease cellular ATP level. These findings demonstrated the availability and effectiveness of the Network Target strategy for identifying the comprehensive pharmacological functions of natural products.

  • Network Target for screening synergistic drug combinations with application to traditional Chinese medicine.
    BMC systems biology, 2011
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge. In this work, we proposed for the first time a "Network Target"-based paradigm instead of the traditional "single Target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network Target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological Network as a therapeutic Target and assumes that the relationship among agents can be transferred to Network interactions among the molecular level entities (Targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background Networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a Network Target perspective. NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The Network Target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

  • Network Target for screening synergistic drug combinations with application to traditional chinese medicine
    BMC Systems Biology, 2011
    Co-Authors: Shao Li, Bo Zhang, Ningbo Zhang
    Abstract:

    Background Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.

Yu-hen Hu - One of the best experts on this subject based on the ideXlab platform.

  • Distributed particle filters for wireless sensor Network Target tracking
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005
    Co-Authors: Xiaohong Sheng, Yu-hen Hu
    Abstract:

    We propose two distributed particle filters to estimate and track the moving Targets in a wireless sensor Network. The observations by the sensors are divided into a set of disjoint uncorrelated cliques. The first distributed algorithm runs the local particle filters sequentially at each clique. The second distributed algorithm runs the local particle filters in parallel to obtain the local sufficient statistics, and then send these statistics to a centralized location through multi-hops to obtain the final estimates. The two distributed algorithms are both almost surely convergent. In addition, we proposed to use the local Gaussian mixture model (GMM) to approximate the posteriori distribution obtained from the local particle filter. By propagating the GMM parameters rather than belief, we achieve significant bandwidth and power consumption reduction. Very promising simulation results are reported as well.

  • ICASSP (4) - Distributed particle filters for wireless sensor Network Target tracking
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005
    Co-Authors: Xiaohong Sheng, Yu-hen Hu
    Abstract:

    We propose two distributed particle filters to estimate and track the moving Targets in a wireless sensor Network. The observations by the sensors are divided into a set of disjoint uncorrelated cliques. The first distributed algorithm runs the local particle filters sequentially at each clique. The second distributed algorithm runs the local particle filters in parallel to obtain the local sufficient statistics, and then send these statistics to a centralized location through multi-hops to obtain the final estimates. The two distributed algorithms are both almost surely convergent. In addition, we proposed to use the local Gaussian mixture model (GMM) to approximate the posteriori distribution obtained from the local particle filter. By propagating the GMM parameters rather than belief, we achieve significant bandwidth and power consumption reduction. Very promising simulation results are reported as well.

Alexandros Iosifidis - One of the best experts on this subject based on the ideXlab platform.

  • Supervised subspace learning based on deep randomized Networks
    2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016
    Co-Authors: Alexandros Iosifidis, Moncef Gabbouj
    Abstract:

    In this paper, we propose a supervised subspace learning method that exploits the rich representation power of deep feedforward Networks. In order to derive a fast, yet efficient, learning scheme we employ deep randomized neural Networks that have been recently shown to provide good compromise between training speed and performance. For optimally determining the learnt subspace, we formulate a regression problem where we employ Target vectors designed to encode both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by the Network's last hidden layer outputs. We experimentally show that the proposed approach is able to outperform deep randomized neural Networks trained by using the standard Network Target vectors.

  • Extreme learning machine based supervised subspace learning
    Neurocomputing, 2015
    Co-Authors: Alexandros Iosifidis
    Abstract:

    This paper proposes a novel method for supervised subspace learning based on Single-hidden Layer Feedforward Neural Networks. The proposed method calculates appropriate Network Target vectors by formulating a Bayesian model exploiting both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by the Network?s hidden layer outputs. After the calculation of the Network Target vectors, Extreme Learning Machine-based neural Network training is applied and classification is performed using a Nearest Neighbor classifier. Experimental results on publicly available data sets show that the proposed approach consistently outperforms the standard ELM approach, as well as other standard methods.