Product Code Database
In the past few decades, leaps in genomic research have led to massive amounts of biological data. As a result, bioinformatics was created as the convergence of genomics, biotechnology, and information technology, while concentrating on biological data.
Biological data has also been difficult to define, as bioinformatics is a wide-encompassing field. Further, the question of what constitutes as being a living organism has been contentious, as "alive" represents a nebulous term that encompasses molecular evolution, biological modeling, biophysics, and systems biology. From the past decade onwards, bioinformatics and the analysis of biological data have been thriving as a result of leaps in technology required to manage and interpret data. It is currently a thriving field, as society has become more concentrated on the acquisition, transfer, and exploitation of bioinformatics and biological data.
Moreover, raw biological sequence data usually refers to DNA, RNA, and .
Biological data can also be described as data on biological entities. For instance, characteristics such as: sequences, graphs, geometric information, scalar and vector fields, patterns, constraints, images, and spatial information may all be characterized as biological data, as they describe features of biological beings. In many instances, biological data are associated with several of these categories. For instance, as described in the National Institute of Health's report on Catalyzing Inquiry at the Interface of Computing and Biology, a protein structure may be associated with a one-dimensional sequence, a two-dimensional image, and a three dimensional structure, and so on.
Biohacking can be carried out by synthesizing malicious DNA and inserted into biological samples. Researchers have established scenarios that demonstrate the threat of biohacking, such as a hacker reaching a biological sample by hiding malicious DNA on common surfaces, such as lab coats, benches, or rubber gloves, which would then contaminate the genetic data.
However, the threat of biohacking may be mitigated by using similar techniques that are used to prevent conventional injection attacks. Clinicians and researchers may mitigate a bio-hack by extracting genetic information from biological samples, and comparing the samples to identify material unknown materials. Studies have shown that comparing genetic information with biological samples, to identify bio-hacking code, has been up to 95% effective in detecting malicious DNA inserts in bio-hacking attacks.
However, ambiguity surrounding the definition of "personal data" in the text of the GDPR, especially regarding biological data, has led to doubts on whether regulation will be enforced for genetic samples. Article 4(1) states that personal data is defined as "Any information relating to an identified or identifiable natural person ('data subject')"
Reinforcement learning, a term stemming from behavioral psychology, is a method of problem solving by learning things through trial and error. Reinforcement learning can be applied to biological data, in the field of omics, by using RL to predict bacterial genomes.
Other studies have shown that reinforcement learning can be used to accurately predict biological sequence annotation. (2010). 9781424483914 ISBN 9781424483914
Deep Learning (DL) architectures are also useful in training biological data. For instance, DL architectures that target pixel levels of biological images have been used to identify the process of mitosis in histological images of the breast. DL architectures have also been used to identify nuclei in images of breast cancer cells.
Researchers have pointed out that with increasing health care costs and tremendous amounts of underutilized data, health information technologies may be the key to improving the efficiency and quality of healthcare.
Legal scholars have pointed towards three primary concerns for increasing litigation pertaining to biomedical databases. First, data contained in biomedical databases may be incorrect or incomplete. Second, systemic biases, which may arise from researcher biases or the nature of the biological data, may threaten the validity of research results. Third, the presence of data mining in biological databases can make it easier for individuals with political, social, or economic agendas to manipulate research findings to sway public opinion.
An example of database misuse occurred in 2009 when the Journal of Psychiatric Research published a study that associated abortion to psychiatric disorders. The purpose of the study was to analyze associations between abortion history and psychiatric disorders, such as anxiety disorders (including panic disorder, PTSD, and agoraphobia) alongside substance abuse disorders and mood disorders.
However, the study was discredited in 2012 when scientists scrutinized the methodology of the study and found it severely faulty. The researchers had used "national data sets with reproductive history and mental health variables" to produce their findings. However, the researchers had failed to compare women (who had unplanned pregnancies and had abortions) to the group of women who did not have abortions, while focusing on psychiatric problems that occurred after the terminated pregnancies. As a result, the findings which appeared to give scientific credibility, gave rise to several states enacting legislation that required women to seek counseling before abortions, due to the potential of long-term mental health consequences.
Another article, published in the New York Times, demonstrated how Electronic Health Records (EHR) systems could be manipulated by doctors to exaggerate the amount of care they provided for purposes of Medicare reimbursement.
While researchers struggle with technological issues in sharing data, social issues are also a barrier to sharing biological data. For instance, clinicians and researchers face unique challenges to sharing biological or health data within their medical communities, such as privacy concerns and patient privacy laws such as HIPAA.
Within the field of biomedical research, data sharing has been promoted as an important way for researchers to share and reuse data in order to fully capture the benefits towards personalized and precision medicine.
|
|
