Recent Advances in Bioinformatics
- Computational proteomics: Mass Spectrometry (MS) is an important technology in proteomics studies. It is more useful for its increasing precision and possibility to automate the pipeline of proteomics analysis and yield large-scale high-throughput experiments. The major application of computational proteomics in the medical and biological field leads to an increase in the volume of available proteomics data using efficient algorithms, management and analysis of novel proteomics data, and visualization techniques. High throughput data production and collection creates a new challenge in data handling and reusability. The increasing availability of tools and data opens new opportunities which can utilize only through various applications of computer science, machine learning, knowledge discovery, statistics, and signal processing techniques. The goal of computational proteomics is to infer knowledge models from the examination of a biological sample, both wet and dry and results from several tools, methods, databases, and algorithms.
- Next-generation sequencing and sequence analysis: The NGS involves high-throughput and massively parallel sequencing technology which implemented revolutionary changes in biological science. NGS enables scientists and researchers with a wide range of applications in biological as well as clinical applications. Millions of DNA reads can be sequenced in a single assay at minimum cost through NGS. Apart from this, NGS has various applications including variant analysis using whole genome or exome sequencing, transcriptome profile analysis, microbial profiling, and detection of genetic biomarkers for disease diagnosis. NGS has been a recommended strategy to characterize different organisms.
Metagenomics also known as microbial Eco-genomics is the sequencing and analysis of complex genetic material derived directly from clinical or environmental samples to scrutinize the population of microorganisms present. Recently, metagenomic next-generation sequencing (mNGS) has arisen as sensitive technology which is capable to identify pathogenic organisms from a human sample. mNGS provides detailed sequencing of the total DNA or RNA content of a microbiome facilitating easy diagnosis.
Machine learning in NGS helps to estimate the number of possibilities in a specific problem by continuous learning. By providing new insights into data analysis, ML has revolutionized traditional statistical methods and techniques. The results supplied by ML algorithms are validated using matrices.
- High performance in Bioinformatics: HPC (High-Performance Computing) systems are now evolving to fulfill the needs of Bioinformatics by developing new hardware and software products. Data manipulation strategies in Bioinformatics help to find better treatments and diagnoses for major diseases. With the advancement in technology and HPC evolution, it is now possible to get each detail about cellular biology and observe cell development pathways.
Biomedical text mining helps to provide resources and tools to scientists and researchers and facilitates them to get and analyze biological data to discover new knowledge. It can extract various biological information, such as gene or protein information, regulation of gene expression, genetic polymorphism, epigenetic information, and the relationship between gene and disease. Different methods of text mining are proposed for extracting the information that varies in their degree of reliance on dictionaries, statistical and knowledge-based approaches, machine learning algorithms, and decision trees.
Biomedical data integration is essential to address medical problems. Integration of the description of data and storage, followed by normalization across various experiments would be a prerequisite to facilitate the procedure of knowledge extraction.
- Biomedical Engineering: Biomedical Engineering is one of the major applications of the problem-solving techniques of engineering to biology and medicine. From diagnosis and analysis to treatment and recovery, it entered the public conscience through the proliferation of implantable medical devices, such as pacemakers, to more futuristic technologies like stem cell engineering or 3D printing of biological organs. It contributes to the development of revolutionary and life-saving applications such as surgical robots, advanced prosthetics, kidney dialysis, artificial organs, and new pharmaceutical drugs.
- E-Health: The advances in information technology help researchers and scientists to improve public health, the healthcare system, and the biomedical field. Bioinformatics and analytics deal with analytics and interpretation, storage development, and optimization of large biomedical data. The revolution in computational proficiency introduces the concept of digital health records which enables the development of a rich data warehouse. Different types of computational tools are used to analyze large biological data to understand the disease and are useful to relate with health care data. Artificial Intelligence (AI) is one of the advanced technologies aimed to replace human intelligence. Drug repositioning, molecular interactions, structure and function analysis, and Protein remote homology detection are a few applications of AI in Bioinformatics.
- Mathematical Modeling of Metabolic Processes: Mathematical modeling is used to explain cell interactions and their impact on cell metabolism. A mathematical model is a simplification of an actual phenomenon. So, it is possible to develop various mathematical models for the same phenomenon, based on the objectives of the model or available parameters. There are many mathematical models for the description of cellular functions, with the expansion in experimental data within biology. Hence, Bioinformatics plays a prominent role in assigning function to new genes, in the field of functional genomics. Biotechnology focuses on cellular metabolism, as it may be exploited to produce different compounds having various applications as materials, pharmaceuticals, or food additives. Metabolism of living cells is subject to control and regulatory mechanisms which are not completely elucidated and difficult to quantify. Hence, the establishment of fully mechanical models is not possible, and all models are based on simplification.
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