Biological data

Biological data works closely with bioinformatics, which is a recent discipline focusing on addressing the need to analyze and interpret vast amounts of genomic data. 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. == Types of biological data ==

Biological data can be extracted for use in the domains of omics, bio-imaging, and medical imaging. Life scientists value biological data to provide molecular details in living organisms. Tools for DNA sequencing, gene expression (GE), bio-imaging, neuro-imaging, and brain-machine interfaces are all domains that utilize biological data, and model biological systems with high dimensionality. Moreover, raw biological sequence data usually refers to DNA, RNA, and amino acids. 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. == Bio-hacking and privacy threats ==

Bio-hacking Bio-computing attacks have become more common as recent studies have shown that common tools may allow an assailant to synthesize biological information which can be used to hijack information from DNA-analyses. The threat of biohacking has become more apparent as DNA-analysis increases in commonality in fields such as forensic science, clinical research, and genomics. 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. Moreover, concerns arise as some countries recognize genomic data as personal data (and apply data protection rules) while other countries regard the samples in terms of physical matter and do not apply the same data protection laws to genomic samples. The forthcoming General Data Protection Regulation (GDPR) has been cited as a potential legal instrument that may better enforce privacy regulations in bio-banking and genomic research. == Applications of deep learning to biological data ==

As a result of rapid advances in data science and computational power, life scientists have been able to apply data-intensive machine learning methods to biological data, such as deep learning (DL), reinforcement learning (RL), and their combination (deep RL). These methods, alongside increases in data storage and computing, have allowed life scientists to mine biological data and analyze data sets that were previously too large or complex. Deep Learning (DL) and reinforcement learning (RL) have been used in the field of omics research Other studies have shown that reinforcement learning can be used to accurately predict biological sequence annotation. 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. == Challenges to data mining in biomedical informatics ==

Complexity The primary problem facing biomedical data models has typically been complexity, as life scientists in clinical settings and biomedical research face the possibility of information overload. However, information overload has often been a debated phenomenon in medical fields. Computational advances have allowed for separate communities to form under different philosophies. For instance, data mining and machine learning researchers search for relevant patterns in biological data, and the architecture does not rely on human intervention. However, there are risks involved when modeling artifacts when human intervention, such as end user comprehension and control, are lessened. 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. 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" 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. == Biomedical data sharing ==

Sharing biomedical data has been touted as an effective way to enhance research reproducibility and scientific discovery. 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. Attitudes towards data sharing According to a 2015 study as an important way for researchers to share and reuse data in order to fully capture the benefits towards personalized and precision medicine. == References ==