scholarly journals The Complex Structure of the Pharmacological Drug–Disease Network

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1139
Author(s):  
Irene López-Rodríguez ◽  
Cesár F. Reyes-Manzano ◽  
Ariel Guzmán-Vargas ◽  
Lev Guzmán-Vargas
Keyword(s):  
Complex Structure ◽  
New Drugs ◽  
Connected Components ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Active Ingredients ◽  
Random Removal ◽  
Random Failures ◽  
Direct Attack ◽  
Common Target

The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures).

scholarly journals Accelerating High-Throughput Screening for Structural Materials with Production Management Methods

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1330 ◽  
Author(s):  
Alexander Bader ◽  
Finn Meiners ◽  
Kirsten Tracht
Keyword(s):  
High Throughput ◽  
Material Flow ◽  
High Throughput Screening ◽  
Production Management ◽  
Waiting Times ◽  
Complex Structure ◽  
New Drugs ◽  
Order Processing ◽  
Speed Up ◽  
Process Trial

High-throughput screenings are widely accepted for pharmaceutical developments for new substances and the development of new drugs with required characteristics by evolutionary studies. Current research projects transfer this principle of high-throughput testing to the development of metallic materials. In addition to new generating and testing methods, these types of high-throughput systems need a logistical control and handling method to reduce throughput time to get test results faster. Instead of the direct material flow found in classical high-throughput screenings, these systems have a very complex structure of material flow. The result is a highly dynamic system that includes short-term changes such as rerun stations, partial tests, and temporarily paced sequences between working systems. This paper presents a framework that divides the actions for system acceleration into three main sections. First, methods for special applications in high-throughput systems are designed or adapted to speed up the generation, treatment, and testing processes. Second, methods are needed to process trial plans and to control test orders, which can efficiently reduce waiting times. The third part of the framework describes procedures for handling samples. This reduces non-productive times and reduces order processing in individual lots.

Continual representation learning for evolving biomedical bipartite networks

2021 ◽  
Author(s):  
Kishlay Jha ◽  
Guangxu Xun ◽  
Aidong Zhang
Keyword(s):  
Network Structure ◽  
Learning Strategy ◽  
Structure Learning ◽  
Fundamental Problem ◽  
Representation Learning ◽  
Research Area ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Straightforward Application ◽  
Low Dimensional

Abstract Motivation Many real-world biomedical interactions such as ‘gene-disease’, ‘disease-symptom’ and ‘drug-target’ are modeled as a bipartite network structure. Learning meaningful representations for such networks is a fundamental problem in the research area of Network Representation Learning (NRL). NRL approaches aim to translate the network structure into low-dimensional vector representations that are useful to a variety of biomedical applications. Despite significant advances, the existing approaches still have certain limitations. First, a majority of these approaches do not model the unique topological properties of bipartite networks. Consequently, their straightforward application to the bipartite graphs yields unsatisfactory results. Second, the existing approaches typically learn representations from static networks. This is limiting for the biomedical bipartite networks that evolve at a rapid pace, and thus necessitate the development of approaches that can update the representations in an online fashion. Results In this research, we propose a novel representation learning approach that accurately preserves the intricate bipartite structure, and efficiently updates the node representations. Specifically, we design a customized autoencoder that captures the proximity relationship between nodes participating in the bipartite bicliques (2 × 2 sub-graph), while preserving both the global and local structures. Moreover, the proposed structure-preserving technique is carefully interleaved with the central tenets of continual machine learning to design an incremental learning strategy that updates the node representations in an online manner. Taken together, the proposed approach produces meaningful representations with high fidelity and computational efficiency. Extensive experiments conducted on several biomedical bipartite networks validate the effectiveness and rationality of the proposed approach.

Advancements in Cancer Therapeutics

2021 ◽  
pp. 89-115
Author(s):  
Serda Kecel Gunduz ◽  
Bilge Bicak ◽  
Aysen E. Ozel
Keyword(s):  
Protein Design ◽  
Complex Structure ◽  
Cancer Therapeutics ◽  
New Drugs ◽  
In Vivo Studies ◽  
Drug Induced ◽  
Dimensional Simulation ◽  
Dynamics And Function

In this chapter, computational approaches for the discovery of new drugs that are useful for diagnosis and treatment of disease will be described in three parts. MD technique uniquely supports protein design attempts by giving information about protein dynamics associated with atomic-level descriptions of the relationship between dynamics and function. The purpose of molecular docking is to provide an estimate of the ligand-receptor complex structure using computational methods. By this estimation, the mechanism of drug binding and action are described by determining the three-dimensional simulation of drug and drug-induced macrostructure. ADME characteristics are physicochemically significant descriptors and pharmacokinetically relevant properties used to design more effective drugs and new analogs. As a result, in-silico calculations can provide robust preliminary information as to drug activity and mechanism in the drug production process, as well as in vitro and in vivo studies.

scholarly journals Design process robustness: a bipartite network analysis reveals the central importance of people

2018 ◽  
Vol 4 ◽  
Author(s):  
Sebastiano A. Piccolo ◽  
Sune Lehmann ◽  
Anja Maier
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Bipartite Network ◽  
Design Processes ◽  
Process Robustness ◽  
Highly Sensitive ◽  
Scale Design ◽  
Biomass Power Plant ◽  
Organisational Dynamics ◽  
Random Failures

Design processes require the joint effort of many people to collaborate and work on multiple activities. Effective techniques to analyse and model design processes are important for understanding organisational dynamics, for improving collaboration, and for planning robust design processes, reducing the risk of rework and delays. Although there has been much progress in modelling and understanding design processes, little is known about the interplay between people and the activities they perform and its influence on design process robustness. To analyse this interplay, we model a large-scale design process of a biomass power plant with $100+$ people and ${\sim}150$ activities as a bipartite network. Observing that some people act as bridges between activities organised to form nearly independent modules, in order to evaluate process fragility, we simulate random failures and targeted attacks to people and activities. We find that our process is more vulnerable to attacks to people rather than activities. These findings show how the allocation of people to activities can obscure an inherent fragility, making the process highly sensitive and dependent on specific people. More generally, we show that the behaviour of robustness is determined by the degree distributions, the heterogeneity of which can be leveraged to improve robustness and resilience to cascading failures. Overall, we show that it is important to carefully plan the assignment of people to activities.

An Empirical Analysis of Developer Collaboration Network

2013 ◽  
Vol 303-306 ◽  
pp. 2177-2181
Author(s):  
Cheng Xiang Peng
Keyword(s):  
Open Source Software ◽  
Network Theory ◽  
Collaboration Network ◽  
Node Degree ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Software Projects ◽  
Intrinsic Nature ◽  
Domain Experts ◽  
Social Collaboration

To further verify the uses of bipartite network theory and understand the intrinsic nature in social collaboration network. In this paper, we get the information of open source software projects from Source-Forge web and construct a project management collaboration network by analyzing the data of project and manager. Then, through the ordinary projection two kinds of one-mode network are made and the degree distribution of one-mode network and origin bipartite networks shows a power-law like. Finally we evaluate the node's importance on manager network to acquire the core nodes, namely domain experts, by using the metric of node degree, between and topological potential respectively, and provide some helpful applications.

scholarly journals Generating bipartite networks with a prescribed joint degree distribution

2017 ◽  
Vol 5 (6) ◽  
pp. 839-857 ◽  
Author(s):  
Asma Azizi Boroojeni ◽  
Jeremy Dewar ◽  
Tong Wu ◽  
James M Hyman
Keyword(s):  
Degree Distribution ◽  
Bipartite Network ◽  
Degree Sequences ◽  
Bipartite Networks ◽  
Original Network ◽  
Software Environment ◽  
Target Network ◽  
Disjoint Sets ◽  
New Algorithms ◽  
Partnership Networks

Abstract We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree–degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor–movie networks, author–article networks, co-occurrence networks and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbours of that node that must be preserved when generating a network that reflects the structure of the underling system. Our bipartite $2K$ ($B2K$) algorithms generate an ensemble of networks that preserve prescribed degree sequences for the two disjoint set of nodes in the bipartite network, and the joint-degree distribution that is the distribution of the degrees of all neighbours of nodes with the same degree. We illustrate the effectiveness of the algorithms on a romance network using the NetworkX software environment to compare other properties of a target network that are not directly enforced by the $B2K$ algorithms. We observe that when average degree of nodes is low, as is the case for romance and heterosexual partnership networks, then the $B2K$ networks tend to preserve additional properties, such as the cluster coefficients, than algorithms that do not preserve the joint-degree distribution of the original network.

scholarly journals Detecting Communities in 2-Mode Networks via Fast Nonnegative Matrix Trifactorization

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Liu Yang ◽  
Wang Tao ◽  
Ji Xin-sheng ◽  
Liu Caixia ◽  
Xu Mingyan
Keyword(s):  
Community Structure ◽  
Rapid Development ◽  
Nonnegative Matrix ◽  
Communication Technologies ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Slow Convergence ◽  
Network Information ◽  
Relational Networks ◽  
Multiple Dimensions

With the rapid development of the Internet and communication technologies, a large number of multitype relational networks widely emerge in real world applications. The bipartite network is one representative and important kind of complex networks. Detecting community structure in bipartite networks is crucial to obtain a better understanding of the network structures and functions. Traditional nonnegative matrix factorization methods usually focus on homogeneous networks, and they are subject to several problems such as slow convergence and large computation. It is challenging to effectively integrate the network information of multiple dimensions in order to discover the hidden community structure underlying heterogeneous interactions. In this work, we present a novel fast nonnegative matrix trifactorization (F-NMTF) method to cocluster the 2-mode nodes in bipartite networks. By constructing the affinity matrices of 2-mode nodes as manifold regularizations of NMTF, we manage to incorporate the intratype and intratype information of 2-mode nodes to reveal the latent community structure in bipartite networks. Moreover, we decompose the NMTF problem into two subproblems, which are involved with much less matrix multiplications and achieve faster convergence. Experimental results on synthetic and real bipartite networks show that the proposed method improves the slow convergence of NMTF and achieves high accuracy and stability on the results of community detection.

scholarly journals Alkaloids in the pharmaceutical industry: Structure, isolation and application

2003 ◽  
Vol 57 (10) ◽  
pp. 471-478
Author(s):  
Milan Nikolic ◽  
Sinisa Djordjevic
Keyword(s):  
Pharmaceutical Industry ◽  
Raw Materials ◽  
Ergot Alkaloids ◽  
New Drugs ◽  
Practical Reasons ◽  
Active Ingredients ◽  
Active Components ◽  
Partial Synthesis ◽  
Purine Derivative

By the end of the 18th and the beginning of the 19th century a new era began in medicine, pharmaceutics and chemistry that was strongly connected with alkaloids and alkaloid drugs. Even before that it was known that certain drugs administered in limited doses were medicines, and toxic if taken in larger doses (opium, coke leaves, belladonna roots, monkshood tubers crocus or hemlock seeds). However, the identification, isolation and structural characterization of the active ingredients of the alkaloid drugs was only possible in the mid 20th century by the use of modern extraction equipment and instrumental methods (NMR, X-ray diffraction and others).In spite of continuing use over a long time, there is still great interest in investigating new drugs, potential raw materials for the pharmaceutical industry, as well as the more detailed investigation and definition of bio-active components and the indication of their activity range, and the partial synthesis of new alkaloid molecules based on natural alkaloids. The scope of these investigations, especially in the field of semi-synthesis is to make better use of the bio-active ingredients of alkaloid drugs, i.e. to improve the pharmacological effect (stronger and prolonged effect of the medicine, decreased toxicity and side effects), or to extend or change the applications. A combined classification of alkaloids was used, based on the chemical structure and origin, i.e. the source of their isolation to study alkaloid structure. For practical reasons, the following classification of alkaloids was used: ergot alkaloids, poppy alkaloids, tropanic alkaloids purine derivative alkaloids, carbon-cyclic alkaloids, and other alkaloids. The second part of this report presents a table of general procedures for alkaloid isolation from plant drugs (extraction by water non-miscible solvents, extraction by water-miscible solvents and extraction by diluted acid solutions). Also, methods for obtaining chelidonine and glaucine as hydrochloride bases and salts were presented in more details. Data from leading world pharmacopoeias (Ph. Eur. Ill/s 2000, DAB 1996, USP 23, JP XIII, BP 1993, Ph. Jug. IV) were used in the study of application of the pure alkaloids in pharmaceutical forms with predetermined doses. A comparative study of these data shows that a great number of preparations are produced worldwide based on alkaloids and alkaloids with modified structure. These medicines have found use in modern therapeutic practice in many countries. Most products are produced on the basis of caffeine, theophylline, ephedrine, atropine, scopolamine, reserpine and pilocarpine.

Transmitted Power in a Structure Using the Effective Mass Parameters

2010 ◽  
Author(s):  
Costin D. Untaroiu ◽  
Alexandrina Untaroiu ◽  
Matthew Wagner ◽  
Paul E. Allaire
Keyword(s):  
Power Flow ◽  
Complex Structure ◽  
Low Frequency ◽  
Connected Components ◽  
Vibration Modes ◽  
Lumped Mass ◽  
Know How ◽  
Stiffness Matrices ◽  
Lumped Mass Method ◽  
Fixed Interface

To reduce the vibration levels in a complex structure, the designer often needs to know how the vibrations in one part of a structure are transmitted to other parts at each interface of the connected components. A lumped-mass method and component mode synthesis is used to evaluate the power flow for vibrations in low-frequency range. The model mass and stiffness matrices are portioned into substructures separated by the interfaces whose power flow should be evaluated. The vibration modes of the substructure are divided into constrained and fixed interface modes corresponding to the interface and interior degree of freedoms, respectively. The effective interface mass criterion is used to rank the most dynamic important modes at each interface. The most important modes are preserved in a reduced model for computing the power flow. A numerical example of a linear system is used to illustrate the application of the new technique.

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