Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Vyas, Renu; Bapat, Sanket; Goel, Purva; Karthikeyan, Muthukumarasamy; Tambe, Sanjeev S.; Kulkarni, Bhaskar D.

doi:10.1109/tcbb.2016.2621042

Cited by 16 publications

(10 citation statements)

References 71 publications

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“…Genetic programming [33]- [35] can search the structure of expression tree coding, and by virtue of its idea, the optimization mechanism can be designed to construct the hybrid model. The specific steps are as follows.…”

Section: Optimization Mechanism Of Hybrid Modelmentioning

confidence: 99%

A Genetic Programming-Driven Data Fitting Method

et al. 2020

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Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability. INDEX TERMS Data fitting, hybrid model, genetic programming, tree coding, interpretability.

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Section: Optimization Mechanism Of Hybrid Modelmentioning

confidence: 99%

A Genetic Programming-Driven Data Fitting Method

et al. 2020

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“…Vyas, R. menciona en su investigación (Vyas et al, 2016) que para entender una enfermedad es necesario comprender los mecanismos moleculares subyacentes, como el número de interacciones proteína-proteína el cual es muy limitado en comparación con las secuencias de proteínas disponibles, enfocando su investigación a la enfermedad de diabetes mellitus, propuso un modelo basado en Máquinas de Soporte Vectorial (MSV), para clasificar las huellas estructurales y genómicas correspondientes a la enfermedad y las que no lo son, obteniendo una exactitud de clasificación del 78,2 %.…”

Section: Trabajos Relacionadosunclassified

Proceedings Ciencias Multidisciplinarias

Trejo-Macotela¹

2020

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Ninguna parte de este escrito amparado por la Ley Federal de Derechos de Autor, podrá ser reproducida, transmitida o utilizada en cualquier forma o medio, ya sea gráfico, electrónico o mecánico, incluyendo, pero sin limitarse a lo siguiente: Citas en artículos y comentarios bibliográficos, de compilación de datos periodísticos radiofónicos o electrónicos. Para los efectos de los artículos 13, 162,163 fracción I, 164 fracción I, 168, 169,209 fracción III y demás relativos de la Ley Federal de Derechos de Autor. Violaciones: Ser obligado al procesamiento bajo ley de copyright mexicana. El uso de nombres descriptivos generales, de nombres registrados, de marcas registradas, en esta publicación no implican, uniformemente en ausencia de una declaración específica, que tales nombres son exentos del protector relevante en leyes y regulaciones de México y por lo tanto libre para el uso general de la comunidad científica internacional. PCM es parte de los medios de ECORFAN-México, S.C, E: 94-443.F:008-(www.ecorfan.org).

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“…The symbolic nature of GP solutions and its flexible representation make GP a very suitable approach for symbolic regression. There are many successful applications of GP for symbolic regression (Vyas et al 2018).…”

Section: Ec For Symbolic Regressionmentioning

confidence: 99%

A survey on evolutionary machine learning

Al-Sahaf

Chen

et al. 2019

Journal of the Royal Society of New Zealand

193

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Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of AI based on the idea that systems can learn from data, identify hidden patterns, and make decisions with little/minimal human intervention. Evolutionary computation is an umbrella of population-based intelligent/learning algorithms inspired by nature, where New Zealand has a good international reputation. This paper provides a review on evolutionary machine learning, i.e., evolutionary computation techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning. The paper also provides a brief review of evolutionary learning applications, such as supply chain and manufacturing for milk/dairy, wine and seafood industries, which are important to New Zealand. Finally, the paper presents current issues with future perspectives in evolutionary machine learning.

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Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Cited by 16 publications

References 71 publications

A Genetic Programming-Driven Data Fitting Method

A Genetic Programming-Driven Data Fitting Method

Proceedings Ciencias Multidisciplinarias

A survey on evolutionary machine learning

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