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Advancement in Gene Delivery
Published in Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Bioinformatics Tools and Big Data Analytics for Patient Care, 2023
Shilpa Rawat, Akash Chauhan, Rishabha Malviya, Md. Aftab Alam, Swati Verma, Shivkanya Fuloria
Leroy Hood and Marvin Carruthers (MC) identified the genomes of 579 humans, not only mapping in situ hybridization but also inventing an automated procedure of DNA sequencing; this project and related organization were founded in 1988. Huang et al. used the above principle in molecular biology investigations in 2000. Thus, this software is a fusion of evaluating biological signals (BS) and generating databases relating to the structures and protein sequencing set-up of the European Molecular Biology Laboratory and GenBank (GB). According to recent research, this program is to target and enclose every detailed study of protein structure analysis with the functional detail of the protein or gene [1, 2]. In 1970, Paulein Hogewag and Bew Heasper coined the term “bioinformatics.” Bioinformatics was described as ‘the science of informatics phenomena in biology.” Bioinformatics is a mix of two terms: bio + information technology, implying that there is an application for biological information. Computational systems aid in the understanding of biological data in bioinformatics. Examples of biological data include DNA and proteins. Bioinformatics is an interdisciplinary area that incorporates biology, mathematics, computer science, chemistry, physics, and information engineering. This field of research is capable of comprehending biological facts. This technology assists in the storage, retrieval, manipulation, and sharing of biological data [3].
ŠUnderstanding Artificial Intelligence (AI)
Published in Louis J. Catania, AI for Immunology, 2021
The term informatics, similar to big data analytics, describes the computational science of how to use data, information, and knowledge to improve human health and the delivery of health care services.32 Bioinformatics is thus a biological subdiscipline of informatics that is concerned with the acquisition, storage, analysis, and dissemination of biological data. And from those general disciplines flows the science of “immunoinformatic” (or computational immunology), which is the science that helps to create significant immunological information using bioinformatics software and applications.33 Some of the main areas of immunoinformatics include vaccinology, antibody analyses, predictions regarding specific epitopes for B-cell recognition and T-cell, cancer immunotherapies, and in the field of immunogenomics.
Machine Learning for Solving a Plethora of Internet of Things Problems
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
Sparsh Sharma, Abrar Ahmed, Mohd Naseem, Surbhi Sharma
Bioinformatics is a field of study that combines computer science (CS), statistics, mathematics and engineering for analyzing and interpreting biological data [72]. In bioinformatics, one of the most challenging aspects is data. Due to the large volume of patient data, sometimes it's difficult to implement this data as a knowledge for the machine. Working with clean and meaningful data, the machine model accurately predicts disease and recommends prescriptions based on the data [73]. ML covers various applications of bioinformatics, such as genomics, proteomics, microarray and system biology. Machine learning is applied for solving various bioinformatics problems: (1) gene finding, (2) gene expression where clustering algorithms are used, (3) population stratification in which PCA, multi-dimension scaling (MDS) and manifold learning techniques have been adopted and (4) DNA sequencing. Konstantina Kourou et al. [74] applied machine learning for determining cancer prognosis.
Bayesian regularized neural network decision tree ensemble model for genomic data classification
Published in Applied Artificial Intelligence, 2018
Bioinformatics is one of the interdisciplinary fields which develop algorithms and methods for analyzing biological data to produce meaningful information (Christopher et al. 2009). During past few decades, in the area of feature-based classification, binary classification problems have been studied extensively but multi category classification has its own importance. Research also shows that multi-class classification is much complex than binary classification and accuracy of classifier drops significantly as the number of classes increases. In literature, various multi-class classifier algorithms have been devised by researchers such as decision tree (DT) (Asria et al. 2016; Patil, Joshi, and Toshniwal 2010), artificial neural network (ANN) (Thein and Tun 2015), support vector machine (SVM) (Bazazeh and Shubair 2017; Barale and Shirke 2016; Megha. and Pareek 2016; Salama, Abdelhalim, and Zeid 2012), and Bayesian regularized artificial network (BRNN) (Burden and Winkler 2008).
Data Stream Management for CPS-based Healthcare: A Contemporary Review
Published in IETE Technical Review, 2022
Sadhana Tiwari, Sonali Agarwal
The following are the different types of data [57–59] used in medical CPS: Clinical data – Data which are collected from clinical assessment and reports are known as clinical data. These data can be gathered during the existing patient care or as a component of a clinical trial programme. Patient/disease registries, administrative data, health surveys, electronic health records, clinical trials data and claims data are examples of clinical data.Self-administered data – This type of data can be collected from paper-based or online questionnaires based on lifestyle health and exposure-related behaviours.Biological data – This type of data can be gathered by performing measurement on biological issues like urine, hair, nails, adipose tissue, blood and placenta.Molecular data – Data related to genome, transcriptome, proteome, metabolome, DNA or protein data are known as molecular data. There are multiple ways to manipulate, edit, simulate analyse these data. Molecular data (DNA data) basically carries genetic information from one generation to another generation in the form of a code.Exposure data – This kind of data can be used to improve risk management decisions. In this competitive edge, high-quality exposure data will be used to ensure the risk in business or research in any area.Modelling data – This kind of data basically includes the data related to estimated exposure and effect parameters.