Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Development of biosensor-based diagnostic systems for breast cancer using biorecognition engineering techniques and machine learning approaches for biomarker discovery(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-07-25) Mayoral Peña, Kalaumari; De Donato Capote, Marcos; emipsanchez; Artzi, Natalie; Víctor Manuel Treviño Alvarado; Alfaro Ponce, Mariel; School of Engineering and Sciences; Campus Monterrey; González Peña, Omar IsraelCancer is the second cause of mortality worldwide, while breast cancer is the second leading cause of global female mortality. Diagnosing and treating breast cancer patients at early stages is relevant for successful treatment and increasing the patient's survival rate. However, early diagnosis of this complex disease is challenging, especially in populations with limited healthcare services. As a result, developing more accessible and accurate diagnostic tools is necessary. The development of low-cost biosensor technologies that have been relevant in the last two decades, but these technologies are still in the process of reaching maturity. For these reasons, we decided to study two promising technologies that can be implemented in cancer biosensor development: 1) biorecognition engineering techniques; 2) machine learning approaches for biomarker discovery. The first technology comprises alternative techniques to generate molecules and molecule-based scaffolds with similar properties to those presented by antibodies. In this study, we presented a systematic analysis of the scientific peer-reviewed literature in the Web of Science from the last two decades to present the fundamentals of this technology and address questions about how it has been implemented in biosensors for cancer detection. The three techniques analyzed were molecularly imprinted polymers, recombinant antibodies, and antibody mimetic molecules. The PRISMA methodology included 131 scientific from 2019 to 2021 for further analysis. The results showed that antibody mimetic molecules technology was the biorecognition technology with the highest number of reports. The most studied cancer types were: multiple, breast, leukemia, colorectal, and lung. Electrochemical and optical detection methods were the most frequently used. Finally, the most analyzed biomarkers and cancer entities in the studies were carcinoembryonic antigen, MCF-7 cells, and exosomes. For the second technology, we developed a novel bioinformatics pipeline that uses machine learning algorithms (MLAs) to identify genetic biomarkers for classifying breast cancer into non-malignant, non-triple-negative, and triple-negative categories. Five Gene Selection Approaches (GSAs) were employed: LASSO (Least Absolute Shrinkage and Selection Operator), Membrane LASSO, Surfaceome LASSO, Network Analysis, and Feature Importance Score (FIS). We implemented three factorial designs to assess the impact of MLAs and GSAs on classification performance (F1 Macro and Accuracy) in both cell lines and patient samples. Using Recursive Feature Elimination (RFE) and Genetic Algorithms (GAs) in the first four GSAs, we reduced the gene count to eight per GSA while maintaining an F1 Macro ≥ 80%. Consequently, 95.5% of our treatments with these gene sets achieved an F1 Macro or Accuracy ranging from 70.3% to 97.2%. As a result, 37 different genes were obtained. We analyzed the 37 genes for their predictive power in terms of five-year survival and relapse-free survival and compared them with genes from four commercial panels. Notably, thirteen genes (MFSD2A, TMEM74, SFRP1, UBXN10, CACNA1H, ERBB2, SIDT1, TMEM129, MME, FLRT2, CA12, ESR1, and TBC1D9) showed significant predictive capabilities for up to five years of survival. TBC1D9, UBXN10, SFRP1, and MME were significant for relapse-free survival after five years. The FOXC1, MLPH, FOXA1, ESR1, ERBB2, and SFRP1 genes also matched those described in commercial panels. The influence of MLA on F1 Macro and Accuracy was not statistically significant. Altogether, the genetic biomarkers identified in this study hold potential for use in biosensors aimed at breast cancer diagnosis and treatment. We concluded that both technologies had demonstrated their utility in cancer biosensor development for vulnerable populations with limited access to healthcare. However, further studies are required, and a long road exists to establish a commercial biosensor. For this reason, we generated a research proposal to develop a biosensor integrating this study's information in an optical and electrochemical sensing platform. Also, some designs of this biosensor and preliminary results are presented.
- Automatic detection of mental health disorders in social media(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-12) Villa Pérez, Miryam Elizabeth; Trejo Rodríguez, Luis Ángel; emipsanchez; González Mendoza, Miguel; Brena Pinero, Ramón Felipe; Moctezuma Ochoa, Daniela; Villaseñor Pineda, Luis; School of Engineering and Sciences; Campus Estado de MéxicoWith the rise of social media, these platforms have emerged as a crucial source of information for studying people's thoughts and behaviors. By using natural language processing and machine learning techniques, prior studies have explored the language of users living with different mental health conditions. However, these efforts have focused on analyzing conditions in isolation, particularly depression, and have relied on English-language data. The goal of this study is to examine the communications of English- and Spanish-speaking Twitter users through traditional and deep learning algorithms to automatically recognize whether they live with one of nine mental health conditions. To achieve that, we created two datasets in English and Spanish. The “diagnosed” set comprises the timeline of 1,500 users who explicitly reported in one or more of their posts having been diagnosed with one of the following: ADHD, Anxiety, Autism, Bipolar, Depression, Eating disorders, OCD, PTSD, and Schizophrenia. The “control” set comprises the timeline of 1,700 randomly selected users who had not disclosed a diagnosis. We extracted a variety of text features from the collected data, such as n-grams, q-grams, Part-of-speech (POS) tags, topic modeling, Linguistic Inquiry and Word Count (LIWC), and word embeddings, and trained traditional machine learning and deep learning classifiers for two tasks: binary classification, to distinguish between diagnosed and non-diagnosed users, and multiclass classification, to identify the specific diagnosis. The performance of the models was analyzed using 5-fold cross-validation, four different classification metrics (AUC, F1-score, Precision, and Recall), and the Friedman non-parametric test with the Finner post-hoc procedure. Overall, XGBoost and CNN performed the best in the two classification tasks. Employing our collected datasets, in binary classification, we achieved an AUC of 0.835 on the Spanish Twitter dataset using n-grams of words from one to three (UBT) and 0.846 on the English Twitter dataset with a 5-gram characters (C5) model. In multiclass classification, we obtained an AUC of 0.747 and 0.697 in the Spanish and English Twitter datasets, respectively. In the second phase of our research, we introduced a model named BiLEMD for the multiclass classification of mental disorders. Our approach adopts a hierarchical detection strategy, where each base model within our framework leverages diverse textual features. We aim to emulate, to some extent, the step-by-step approach employed in human clinical diagnostics. In clinical practice, professionals first determine the presence or absence of a condition before proceeding to specify its type. Although BiLEMD achieved the highest ranking in both the Spanish and English Twitter datasets, statistical significance differences were not observed. Nevertheless, additional analysis revealed that ensembles, including BiLEMD and Stacking, reduce misclassification within the control class. Moreover, BiLEMD exhibits slightly superior performance in terms of AUC and Recall compared to other classifiers. The development of computer-based methods for recognizing and classifying social media user profiles related to different mental health conditions could enhance the performance of applications aimed at early diagnosis and timely treatment.