Hi! I am currently studying big data analysis methods in the field of hereditary diseases and would like to know your opinion. What modern approaches and algorithms, in your opinion, are the most effective for identifying genetic markers associated with rare hereditary pathologies? How important do you think is the integration of multimodal data - for example, genomic, epigenomic and clinical - to improve the accuracy of diagnostics?
Posted by loteko7104 (Member # 38456) on :
I have long been interested in big data analysis in the field of hereditary diseases and I can say that modern methods used today are truly impressive. I consider machine learning algorithms and deep learning methods to be especially effective, which allow us to identify complex relationships between genetic variants and rare pathologies. The most important component of success is the integration of multimodal data - genomic, epigenomic and clinical indicators. Only by combining these sources can we achieve high diagnostic accuracy and understanding of disease mechanisms. Pay attention to the CompassBioInfo platform - it contains advanced tools for complex analysis of biomedical data. They offer convenient solutions for processing large arrays of information and supporting decision-making based on integrated data.
Posted by micagat484 (Member # 38457) on :
Hello! In my experience, modern machine learning methods, especially ensemble algorithms and deep neural networks, play a key role in analyzing big data to identify genetic markers of rare hereditary diseases. They allow us to efficiently process huge amounts of data, identifying complex dependencies that traditional statistical methods may miss. In addition, graph analysis and network bioinformatics methods are good at identifying relationships between genes and pathologies. As for the integration of multimodal data, this is truly fundamental. Combining genomic, epigenomic, and clinical data significantly improves the accuracy and reliability of diagnostics. Such a comprehensive picture helps not only to identify markers with greater confidence, but also to better understand the mechanisms of diseases. Ultimately, it is the multiomic approach that allows us to personalize treatment and improve prognoses for patients with rare hereditary pathologies.
