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Perfil investigador
Esp
Dr. Pedro Pablo González Pérez

Professor
Departament of Applied Mathematics and Systems

Division of Natural Sciences and Engineering


Level 1
Member of the National System of Researchers
(SNII)

Physics-Mathematics and Earth Sciences



Cuajimalpa Campus

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Sustainable Development Goals

• 3 Good Health and Well-Being

• 4 Quality Education

• 17 Partnerships


Research interests

• Bioinformatics and Computational Biology: theories and development of bioinformatics tools
• Modeling and simulation of biological systems and phenomena: cell signaling networks, in silico exploration and in vitro evaluation
• Modeling and simulation of biological systems and phenomena: protein folding, molecular design and foldamers
• Artificial neural networks: Generative Adversarial Networks (GAN) for data augmentation
• Synthetic data and big data: data farming from in silico experimentation

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Research interests

• Bioinformatics and Computational Biology: theories and development of bioinformatics tools
• Modeling and simulation of biological systems and phenomena: cell signaling networks, in silico exploration and in vitro evaluation
• Modeling and simulation of biological systems and phenomena: protein folding, molecular design and foldamers
• Artificial neural networks: Generative Adversarial Networks (GAN) for data augmentation
• Synthetic data and big data: data farming from in silico experimentation

Academic Work

On the following pages you can consult the research work:



Other sites of interest

Consult the research work on other websites:






Some examples of publications

Select the bibliographic reference to consult each publication:


Open Access References UN SDGs
OASánchez-Gutiérrez, M.E., González-Pérez, P.P. (2023).Addressing the class imbalance in tabular datasets from a generative adversarial network approach in supervised machine learning. Journal of Algorithms and Computational Technology,17| 3 |
OASánchez-Gutiérrez, M.E., González-Pérez, P.P. (2022).Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures. Entropy,24(2) | 3 |
OASánchez-Gutiérrez, M.E., González-Pérez, P.P. (2022).Modeling and Simulation of Cell Signaling Networks for Subsequent Analytics Processes Using Big Data and Machine Learning. Bioinformatics and Biology Insights,16| 3 |
González-Pérez, P.P., Sánchez-Gutiérrez, M.E. (2022).Improving the accuracy of multiclass classification in machine learning: A case study in a cell signaling dataset. Intelligent Data Analysis,26(2) 481-500| 3 |
OAOrtiz-González, A., González-Pérez, P.P., Cárdenas-García, M. and 1 more (...) (2022).In silico Prediction on the PI3K/AKT/mTOR Pathway of the Antiproliferative Effect of O. joconostle in Breast Cancer Models. Cancer Informatics,21| 3 |
Beltrán, H.I., Alas-Guardado, S.J., González-Pérez, P.P. (2022).Improving coarse-grained models of protein folding through weighting of polar-polar/hydrophobic-hydrophobic interactions into crowded spaces. Journal of Molecular Modeling,28(4)
OAAlas-Guardado, S.J., González-Pérez, P.P., Beltrán, H.I. (2021).Contributions of topological polar-polar contacts to achieve better folding stability of 2D/3D HP lattice proteins: An in silico approach. AIMS Biophysics,8(3) 291-306
Sánchez-Gutiérrez, M.E., González-Pérez, P.P. (2020).Discriminative neural network pruning in a multiclass environment: A case study in spoken emotion recognition. Speech Communication,12020-30
Alas, S.D.J., González-Pérez, P.P., Beltrán, H.I. (2019).In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones. BioSystems,18131-43
González-Pérez, P.P., Cárdenas-García, M. (2019).In silico modeling and simulation approach for apoptosis caspase pathways. Advances in Intelligent Systems and Computing,80317-26
OACárdenas-García, M., González-Pérez, P.P. (2018).An in silico approach for understanding the complex intercellular interaction patterns in cancer cells. BIOINFORMATICS 2018 - 9th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018,3188-195| 3 |
González-Pérez, P.P., Cárdenas-García, M. (2018).Inspecting the role of PI3K/AKT signaling pathway in cancer development using an in silico modeling and simulation approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),1081383-95| 3 |
González-Pérez, P.P., Orta, D.J., Peña, I. and 4 more (...) (2017).A Computational Approach to Studying Protein Folding Problems Considering the Crucial Role of the Intracellular Environment. Journal of Computational Biology,24(10) 995-1013
Alas, S.J., González-Pérez, P.P. (2016).Simulating the folding of HP-sequences with a minimalist model in an inhomogeneous medium. BioSystems,142-14352-67
González Pérez, P.P., Omicini, A., Sbaraglia, M. (2013).A biochemically inspired coordination-based model for simulating intracellular signalling pathways. Journal of Simulation,7(3) 216-226

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Courses offered

*Courses are conducted in spanish

Num.Trim.Course NameLevel
1
23O
Proyecto Terminal IIILicenciatura
2
23O
Datos a Gran EscalaLicenciatura
3
23O
Fundamentos de Ingeniería de SoftwareLicenciatura
4
23O
Tópicos Especiales de Sistemas ComputacionalesLicenciatura
5
23I
Proyecto de Ingeniería de Software ILicenciatura
6
23I
Proyecto de Ingeniería de Software IILicenciatura
7
23I
Proyecto Terminal IIILicenciatura
8
22O
Fundamentos de Ingeniería de SoftwareLicenciatura
9
22O
Desarrollo de Software a Gran EscalaLicenciatura
10
22O
Proyecto Terminal IILicenciatura
11
22P
Tópicos Especiales de Sistemas ComputacionalesLicenciatura
12
22P
Proyecto Terminal ILicenciatura
13
22P
Datos a Gran EscalaLicenciatura
14
22I
Proyecto de Ingeniería de Software ILicenciatura
15
22I
Proyecto de Ingeniería de Software IILicenciatura
16
22I
Proyecto Terminal IIILicenciatura
17
21O
Proyecto Terminal IILicenciatura
18
21O
Desarrollo de Software a Gran EscalaLicenciatura
19
21P
Proyecto Terminal ILicenciatura
20
21P
Proyecto Terminal IIILicenciatura
21
21P
Patrones de Diseño y Arquitectura de SoftwareLicenciatura
22
21I
Proyecto de Ingeniería de Software ILicenciatura
23
21I
Proyecto de Ingeniería de Software ILicenciatura
24
21I
Proyecto de Ingeniería de Software IILicenciatura
25
21I
Proyecto Terminal IILicenciatura
26
21I
Proyecto Terminal IIILicenciatura
27
21I
Bioinformática IIPosgrado
28
20O
Proyecto Terminal ILicenciatura
29
20O
Proyecto Terminal IILicenciatura
30
20O
Datos a Gran EscalaLicenciatura
31
20O
Bioinformática IPosgrado
32
20P
Análisis y Diseño Orientado a ObjetosLicenciatura
33
20P
Análisis y Diseño Orientado a ObjetosLicenciatura
34
20P
Proyecto Terminal ILicenciatura
35
20I
Proyecto de Ingeniería de Software ILicenciatura
36
20I
Proyecto de Ingeniería de Software IILicenciatura
37
20I
Proyecto Terminal IILicenciatura
38
20I
Proyecto Terminal IIILicenciatura
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Universidad Autónoma Metropolitana, 2024.