What methodology can be employed to find biomarkers in omics studies?4 answersTensor decomposition methods, particularly based on tensor decomposition (TD), are increasingly popular for biomarker discovery in omics studies due to the complexity of integrating multi-omics data. Deep learning-based methodologies, such as biologically informed neural networks (BINNs), have been introduced to improve biomarker identification and pathway analysis by incorporating protein abundances and associated pathways. Additionally, statistical frameworks like OmicsLonDA have been developed to identify temporal omics biomarkers in longitudinal studies, addressing challenges like non-uniform sampling intervals and missing data points. Furthermore, symbolic-regression-based algorithms like the QLattice have shown promise in generating high-performing and interpretable biomarker signatures from clinical omics data sets. These diverse methodologies offer valuable tools for discovering biomarkers in omics studies across different research contexts.
What computational methods are commonly used to identify biomarkers in volatolomic studies?4 answersComputational methods commonly used to identify biomarkers in volatolomic studies include factorization machine-based deep neural networks with binary pairwise encoding, ensemble-based feature selection applications, and methods involving intensity models for image-derived biomarkers. These methods leverage advanced algorithms to analyze complex datasets, considering interdependence of features, mapping raw features into embedding vectors, and utilizing wide and deep feature interdependence to identify disease-related biomarkers effectively. The factorization machine-based approach, specifically the DFMbpe model, has shown superior performance in biomarker identification compared to state-of-the-art models, emphasizing both low-order and high-order feature interactions simultaneously. Additionally, the molecular feature selection tool has demonstrated success in identifying potential biomarkers with high accuracy using a significantly smaller set of genes, showcasing its efficiency in biomarker discovery studies.
What are methods can discover biosurfactants?5 answersVarious methods can be employed to discover biosurfactants, including haemolysis, axisymmetric drop shape analysis, rapid drop collapsing, colorimetry, thin layer chromatography. Screening tests like surface tension measurement, emulsification index, oil displacement, drop collapse, and haemolysis are commonly used to detect biosurfactant activity. Techniques such as the hemolysis test, parafilm M test, lipase production test, CTAB agar plate method, and emulsification assay are routinely utilized for screening biosurfactant-producing strains. Isolation and characterization of biosurfactant-producing bacteria from oil-contaminated soil involve methods like oil spreading assay, blood agar hemolysis, emulsification assay, and foaming activity. These diverse methods play a crucial role in identifying and studying the properties of biosurfactants.
How is mass spectrometry used to identify biomarkers?5 answersMass spectrometry is used to identify biomarkers by combining it with various omics techniques such as proteomics, lipdomics, and metabolomics. These techniques allow for the analysis of different types of molecules in biological samples. Machine learning and deep learning methods are also employed to analyze the mass spectrometry data and identify specific diagnostic and prognostic biomarkers for diseases like COVID-19. To overcome the challenges of detecting low-abundance biomarkers in complex proteomes, microfluidics devices are used for protein enrichment, and mass spectrometry is used for detection due to its high sensitivity and specificity. In the case of osteoarthritis, mass spectrometry-based proteomics is used to identify novel serum biomarkers, and statistical analysis techniques like principal component analysis and sparse partial least squares regression discriminant analysis are employed for biomarker selection. Mass spectrometry is also utilized in the identification of peptides and proteins involved in Alzheimer's disease, aiding in early diagnosis, disease progression monitoring, and discrimination from other diseases.
How can computational biology help discover biomarkers?5 answersComputational biology plays a crucial role in discovering biomarkers by utilizing advanced techniques such as nature-inspired intelligent computing (NIIC). NIIC approaches, including artificial intelligence and deep learning, can predict potential biomarkers and drug targets by analyzing disease biomarkers and understanding their molecular mechanisms. These approaches offer a complementary value to experimental analyses, expediting the study of biomarker functions. Additionally, computational methods like molecular feature selection tools enable the identification of candidate biomarkers in complex high-dimensional datasets. These tools have been successfully applied to identify immune-related genes as potential biomarkers for prostate adenocarcinoma subtypes. Furthermore, bioinformatics resources and tools are used to validate and discover ovarian cancer biomarkers, aiding in the development of effective treatment strategies. Quantum computing and quantum AI, such as Quantum Neural Networks (QNNs), provide an economical solution for biomarker discovery tasks by scoring candidate sets using the Maximum Relevance, Minimum Redundancy (mRMR) criteria.
How can machine learning be used to discover biomarkers?5 answersMachine learning can be used to discover biomarkers by analyzing patient data, genomic data, and imaging data to identify molecular signatures that distinguish cancer patients from healthy patients. Machine learning techniques such as deep learning and support vector machines can identify complex patterns in data and classify data to identify relevant biomarkers. By using machine learning algorithms, biomarkers that accurately detect and diagnose cancer can be identified, leading to more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. Machine learning methods can also improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid human bias. Additionally, machine learning models like factorization machine-based deep neural networks can consider the interdependence of features and identify disease-related biomarkers. Overall, machine learning has the potential to revolutionize biomarker discovery and play a crucial role in personalized medicine.