Ana Luisa Romero-Pimentel
Biographic Data
| ID | 7954264 |
|---|---|
| NAME | Ana Luisa Romero-Pimentel |
| GIVEN NAMES | Ana Luisa |
| FAMILY NAME | Romero-Pimentel |
| SIGNATURE | ROMERO-PIMENTEL A L |
| AFFILIATIONS | National Institute of Genomic Medicine |
| ORCID | 0000-0002-4530-5364 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide
Our exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways as key processes perturbed in the DLPFC of suicides. Regarding ML models, KNeighborsClassifier was the best predicting conditions. Here we show that these proteins of the DLPFC may help to identify brain processes associated with suicide and they could be validated as potential biomarkers of this outcome
Demographic and Clinical Characteristics of Completed Suicides in Mexico City 2014–2015
Objective: To analyze sex differences in demographic and clinical characteristics of individuals who died by suicide in Mexico City. Method: Statistical analysis of residents of Mexico City whose cause of death was suicide, during two years period from January 2014 to December 2015, with a coroner's report. Suicide mortality rates were calculated by age, sex, and location within the city. The Chi-squared test was used to assess statistical differ…
No prominent works on this page.
Demographic and Clinical Characteristics of Completed Suicides in Mexico City 2014–2015
Objective: To analyze sex differences in demographic and clinical characteristics of individuals who died by suicide in Mexico City. Method: Statistical analysis of residents of Mexico City whose cause of death was suicide, during two years period from January 2014 to December 2015, with a coroner's report. Suicide mortality rates were calculated by age, sex, and location within the city. The Chi-squared test was used to assess statistical differ…
Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide
Our exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways as key processes perturbed in the DLPFC of suicides. Regarding ML models, KNeighborsClassifier was the best predicting conditions. Here we show that these proteins of the DLPFC may help to identify brain processes associated with suicide and they could be validated as potential biomarkers of this outcome
Medicine (2 works) · 14-3-3 protein interactions (1 works) · Bioinformatics (1 works) · Bioinformatics and Genomic Networks (1 works) · Biology (1 works) · Child and Adolescent Psychosocial and Emotional Development (1 works) · Cognition (1 works) · Computational biology (1 works) · Computer Science (1 works) · Concordance (1 works)