Browsing by Author "Liñares Blanco, Jose"
Now showing items 1-13 of 13
-
A review on machine learning approaches and trends in drug discovery
Carracedo-Reboredo, Paula; Liñares Blanco, Jose; Rodríguez-Fernández, Nereida; Cedrón, Francisco; Novoa, Francisco; Carballal, Adrián; Maojo, Víctor; Pazos, A.; Fernández-Lozano, Carlos (Research Network of Computational and Structural Biotechnology, 2021)Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science ... -
Gene Signatures Research Involved in Cancer Using Machine Learning
Liñares Blanco, Jose; Fernández-Lozano, Carlos (M D P I AG, 2019)[Abstract] With the cheapening of mass sequencing techniques and the rise of computer technologies, capable of analyzing a huge amount of data, it is necessary nowadays that both branches mutually benefit. Transcriptomics, ... -
Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes
Fernández-Edreira, Diego; Liñares Blanco, Jose; Fernández-Lozano, Carlos (MDPI AG, 2020-08-27)[Abstract] Previous works have reported different bacterial strains and genera as the cause of different clinical pathological conditions. In our approach, using the fecal metagenomic profiles of newborns, a machine ... -
Integrative Multi-Omics Data-Driven Approach for Metastasis Prediction in Cancer
Fernández-Lozano, Carlos; Liñares Blanco, Jose; Gestal, M.; Dorado, Julián; Pazos, A. (ACM, 2018)[Abstract] Nowadays biomedical research is generating huge amounts of omic data, covering all levels of genetic information from nucleotide sequencing to protein metabolism. In the beginning, data were analyzed independently ... -
Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data
Liñares Blanco, Jose; Fernández-Lozano, Carlos; Seoane Fernández, José Antonio; López-Campos, Guillermo (NLM (Medline), 2021)[Abstract] In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in ... -
Machine learning analysis of TCGA cancer data
Liñares Blanco, Jose; Pazos, A.; Fernández-Lozano, Carlos (PeerJ Inc., 2021)[Abstract] In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas ... -
Machine Learning Analysis of the Human Infant Gut Microbiome Identifies Influential Species in Type 1 Diabetes
Fernández-Edreira, Diego; Liñares Blanco, Jose; Fernández-Lozano, Carlos (Elsevier, 2021)[Abstract] Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due ... -
Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes
Liñares Blanco, Jose; Fernández-Lozano, Carlos; Seoane Fernández, José Antonio; López-Campos, Guillermo (FRONTIERS MEDIA S.A., 2022)[Abstract] Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple ... -
Molecular Docking and Machine Learning Analysis of Abemaciclib in Colon Cancer
Liñares Blanco, Jose; Munteanu, Cristian-Robert; Pazos, A.; Fernández-Lozano, Carlos (BioMed Central Ltd., 2020-07-08)[Abstract] Background - The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is ... -
Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection
Liñares Blanco, Jose; Porto-Pazos, Ana B.; Pazos, A.; Fernández-Lozano, Carlos (Nature, 2018-10-24)[Abstract] Screening and in silico modeling are critical activities for the reduction of experimental costs. They also speed up research notably and strengthen the theoretical framework, thus allowing researchers to ... -
Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment
Liñares Blanco, Jose; Fernández-Lozano, Carlos (M D P I AG, 2019-07-31)[Abstract]The prediction of metabolic activities in silico form is crucial to be able to address all research possibilities without exceeding the experimental costs. In particular, for cancer research, the prediction of ... -
Técnicas de machine learning aplicadas al diagnóstico y tratamiento oncológico de precisión mediante el análisis de datos ómicos
Liñares Blanco, Jose (2021)[Resumen] Gracias al abaratamiento en los costes de secuenciación, cada día se genera una mayor cantidad de datos ómicos capaces de caracterizar el cáncer molecularmente. Grandes consorcios generan gran cantidad de estos ... -
VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria
Fernández Edreira, Diego; Liñares Blanco, Jose; Vázquez del Río, Patricia; Fernández-Lozano, Carlos (Elsevier, 2024)[Abstract]: This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort ...