The Rücker–Markov invariants of complex Bio-Systems: Applications in Parasitology and Neuroinformatics

González-Dı́az, Humberto; Riera-Fernández, Pablo; Munteanu, Cristian R.

doi:10.1016/j.biosystems.2013.02.006

Cited by 15 publications

(9 citation statements)

References 70 publications

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“…It includes mapping drug, target protein, or parasite vaccine epitopes vs. information about cell https://mol2net-07.sciforum.net/ lines, assay organisms, host organisms, bacteria metabolic networks, and parasite spreading networks. [7][8][9][10][11][12] IFPTML has also been applied to NP systems considering NP structure and coating agents, NP synthesis conditions, loaded drug structure, co-therapy loaded drugs, assay conditions, etc. [5,6,13-15] Accordingly, in this work, the IFPTML model for DADNP systems design was developed, including AD and NP components at the same time.…”

Section: Introductionmentioning

confidence: 99%

Towards Dual Antibacterial Drug-Nanoparticle (DADNP) Systems Computational Design

Diéguez-Santana

2021

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 7th Ed.

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The emergence of Multidrug-Resistant (MDR) strains promotes the improvement of Antibacterial Drugs (AD). Some nanoparticles (NP) may be AD carriers, but some have antibacterial activity per se. This opens a window of opportunity for the design of Dual Antibacterial Drug-Nanoparticle (DADNP) systems. DADNP discovery is a slow process due to the high number of combinations of NP vs. AD compounds, assays, etc. Artificial Intelligence/Machine Learning (AI/ML) algorithms that anticipate which potential MOL2NET,

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Section: Introductionmentioning

confidence: 99%

Towards Dual Antibacterial Drug-Nanoparticle (DADNP) Systems Computational Design

Diéguez-Santana

2021

Proceedings of MOL2NET'21, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 7th Ed.

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“…In such a way, we can fit quantitative models able to predict the properties of networks that depend on their structure. The combination of NA and ML can be useful to study the interrelationship between structure and properties of many types of networks including, for example, proteomes, brain cortex, epidemiological, social networks, and so on. − …”

Section: Introductionmentioning

confidence: 99%

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome

2018

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The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans, and many more are organized into larger clusters. This raises intriguing questions previously asked by different authors. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? We define a new kind of network coined as the gene orientation inversion network (GOIN). GOIN's complex network encodes short- and long-range patterns of inversion of the orientation of pairs of gene in the chromosome. We selected Plasmodium falciparum as a case of study due to the high relevance of this parasite to public health (causal agent of malaria). We constructed here for the first time all of the GOINs for the genome of this parasite. These networks have an average of 383 nodes (genes in one chromosome) and 1314 links (pairs of gene with inverse orientation). We calculated node centralities and other parameters of these networks. These numerical parameters were used to study different properties of gene inversion patterns, for example, distribution, local communities, similarity to Erdös-Rényi random networks, randomness, and so on. We find clues that seem to indicate that gene orientation inversion does not follow a random pattern. We noted that some gene communities in the GOINs tend to group genes encoding for RIFIN-related proteins in the proteome of the parasite. RIFIN-like proteins are a second family of clonally variant proteins expressed on the surface of red cells infected with Plasmodium falciparum. Consequently, we used these centralities as input of machine learning (ML) models to predict the RIFIN-like activity of 5365 proteins in the proteome of Plasmodium sp. The best linear ML model found discriminates RIFIN-like from other proteins with sensitivity and specificity 70-80% in training and external validation series. All of these results may point to a possible biological relevance of gene orientation inversion not directly dependent on genetic sequence information. This work opens the gate to the use of GOINs as a tool for the study of the structure of chromosomes and the study of protein function in proteome research.

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“…They are a clear example of network-like structures with known procedures to calculate the Sh k values 32 – 34 . In this sense, our group reported different ML models that evaluate the structure of parasite-host webs to predict the interactions between species in different networks 35 – 37 . In one of our previous works, special emphasis has been placed on the use of Sh k information measures to codify structural information in this type of ML studies 38 .…”

Section: Introductionmentioning

confidence: 99%

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Barreiro

Munteanu

Cruz-Monteagudo

et al. 2018

Sci Rep

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Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Shk) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.

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The Rücker–Markov invariants of complex Bio-Systems: Applications in Parasitology and Neuroinformatics

Cited by 15 publications

References 70 publications

Towards Dual Antibacterial Drug-Nanoparticle (DADNP) Systems Computational Design

Towards Dual Antibacterial Drug-Nanoparticle (DADNP) Systems Computational Design

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

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