Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

quantities of time-stamped data will provide doctors with important information regarding the progress of the disease. Therefore, systems capable of performing temporal abstraction and reasoning become crucial in this context. Although the use of temporal-reasoning methods requires an intensive knowledge-acquisition effort, data mining has been used in many successful medical applications, including data validation in intensive care, the monitoring of children’s growth, analysis of a diabetic patient’s data, the monitoring of heart-transplant patients, and intelligent anesthesia monitoring.

Data mining has been used extensively in the medical industry. Data visualization and artificial neural networks are especially important areas of data mining applicable in the medical field. For example, NeuroMedicalSystems used neural networks to perform a pap smear diagnostic aid. Vysis Company uses neural networks to perform protein analyses for drug development. The University of Rochester Cancer Center and the Oxford Transplant Center use KnowledgeSeeker, a decision tree-based technology, to help with their research in oncology.

The past decade has seen an explosive growth in biomedical research, ranging from the development of new pharmaceuticals and advances in cancer therapies to the identification and study of the human genome. The logic behind investigating the genetic causes of diseases is that once the molecular bases of diseases are known, precisely targeted medical interventions for diagnostics, prevention, and treatment of the disease themselves can be developed. Much of the work occurs in the context of the development of new pharmaceutical products that can be used to fight a host of diseases ranging from various cancers to degenerative disorders such as Alzheimer’s Disease.

A great deal of biomedical research has focused on DNA-data analysis, and the results have led to the discovery of genetic causes for many diseases and disabilities. An important focus in genome research is the study of DNA sequences since such sequences form the foundation of the genetic codes of all living organisms. What is DNA? Deoxyribonucleic acid, or DNA, forms the foundation for all living organisms. DNA contains the instructions that tell cells how to behave and is the primary mechanism that permits us to transfer our genes to our offspring. DNA is built in sequences that form the foundations of our genetic codes, and that are critical for understanding how our genes behave. Each gene comprises a series of building blocks called nucleotides. When these nucleotides are combined, they form long, twisted, and paired DNA sequences or chains. Unraveling these sequences has become a challenge since the 1950s when the structure of the DNA was first understood. If we understand DNA sequences, theoretically, we will be able to identify and predict faults, weaknesses, or other factors in our genes that can affect our lives. Getting a better grasp of DNA sequences could potentially lead to improved procedures to treat cancer, birth defects, and other pathological processes. Data-mining technologies are only one weapon in the arsenal used to understand these types of data, and the use of visualization and classification techniques is playing a crucial role in these activities.

It is estimated that humans have around 100,000 genes, each one having DNA that encodes a unique protein specialized for a function or a set of functions. Genes controlling production of hemoglobin, regulation of insulin, and susceptibility to Huntington’s chorea are among those that have been isolated in recent years. There are seemingly endless varieties of ways in which nucleotides can be ordered and sequenced to form distinct genes. Any one gene might comprise a sequence containing hundreds of thousands of individual nucleotides arranged in a particular order. Furthermore, the process of DNA sequencing used to extract genetic information from cells and tissues usually produces only fragments of genes. It has been difficult to tell using traditional methods where these fragments fit into the overall complete sequence from which they are drawn. Genetic scientists face the difficult task of trying to interpret these sequences and form hypotheses about which genes they might belong to, and the disease processes that they may control. The task of identifying good candidate gene sequences for further research and development is like finding a needle in a haystack. There can be hundreds of candidates for any given disease being studied. Therefore, companies must decide which sequences are the most promising ones to pursue for further development. How do they determine which ones would make good therapeutic targets? Historically, this has been a process based largely on trial and error. For every lead that eventually turns into a successful pharmaceutical intervention that is effective in clinical settings, there are dozens of others that do not produce the anticipated results. This is a research area that is crying out for innovations that can help to make these analytical processes more efficient. Since pattern analysis, data visualization, and similarity-search techniques have been developed in data mining, this field has become a powerful infrastructure for further research and discovery in DNA sequences. We will describe one attempt to innovate the process of mapping human genomes that has been undertaken by Incyte Pharmaceuticals, Inc. in cooperation with Silicon Graphics.

Incyte Pharmaceuticals, Inc.

Incyte Pharmaceuticals is a publicly held company founded in 1991, and it is involved in high-throughput DNA sequencing and development of software, databases, and other products to support the analysis of genetic information. The first component of their activities is a large database called LiveSeq that contains more than 3 million human-gene sequences and expression records. Clients of the company buy a subscription to the database and receive monthly updates that include all of the new sequences identified since the last update. All of these sequences can be considered as candidate genes that might be important for future genome mapping. This information has been derived from DNA sequencing and bioanalysis of gene fragments extracted from cell and tissue samples. The tissue libraries contain different types of tissues including normal and diseased tissues, which are very important for comparison and analyses.

To help impose a conceptual structure of the massive amount of information contained in LifeSeq, the data has been coded and linked to several levels. Therefore, DNA sequences