All too often, individuals engaged in the biomedical sciences assume that numeric data must be left to the proper authorities (e.g., statisticians and data analysts) who are trained to apply sophisticated mathematical algorithms to sets of data. This is a terrible mistake. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from their own data and to guide the subsequent implementation of robust, mathematical analyses. Volume 2 of Logic and Critical Thinking in the Biomedical Sciences provides readers with a…mehr
All too often, individuals engaged in the biomedical sciences assume that numeric data must be left to the proper authorities (e.g., statisticians and data analysts) who are trained to apply sophisticated mathematical algorithms to sets of data. This is a terrible mistake. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from their own data and to guide the subsequent implementation of robust, mathematical analyses. Volume 2 of Logic and Critical Thinking in the Biomedical Sciences provides readers with a repertoire of deductive non-mathematical methods that will help them draw useful inferences from their own data.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jules J. Berman, Ph.D., M.D. holds degrees from MIT, Temple University, and the University of Miami. He served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, with joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. He later served at the US National Cancer Institute as a medical officer and as program director for pathology informatics in the Cancer Diagnosis Program. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the association's Lifetime Achievement Award.Jules J. Berman, Ph.D., M.D. holds degrees from MIT, Temple University, and the University of Miami. He served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, with joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. He later served at the US National Cancer Institute as a medical officer and as program director for pathology informatics in the Cancer Diagnosis Program. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the association's Lifetime Achievement Award. He has first-authored more than 100 journal articles and has written 18 science books. His most recent titles, published by Elsevier, include: -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 1st edition (2012) -Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information (2013) -Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases (2014) -Repurposing Legacy Data: Innovative Case Studies (2015) -Data Simplification: Taming Information with Open Source Tools (2016) -Precision Medicine and the Reinvention of Human Disease (2018) -Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition (2018) -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 2nd edition (2019)
Inhaltsangabe
1. Learning what counting tells usSection 1.1. Science is mostly about counting stuff Section 1.2. Never count on an accurate count Section 1.3. Large samples cannot compensate for nonrepresentative data Section 1.4. The perils of combining data setsSection 1.5. Compositionality: Why small outnumbers large Section 1.6. Looking at data Section 1.7. Counting mutations Section 1.8. Chromosome length and the frequency of genetic diseases Section 1.9. Counting instances of species Section 1.10. Counting garbage Glossary References 2. Drawing inferences from absences of data values Section 2.1. When the important data is what you do not see Section 2.2. The power of negative thinking Section 2.3. Absence of x-rays emitted by hot cups of coffee Section 2.4. Absence of laboratory findings in SIDS (sudden infant death syndrome) Section 2.5. Absence of lethal toxicity resulting from damage to the epigenome and systems that regulate gene expressionSection 2.6. Absence of deficiency diseases among highly conserved genesSection 2.7. Absence of shared conserved noncoding elements Section 2.8. Absence of animals with built-in wheels Section 2.9. Absence of microcancers Section 2.10. Absence of frogs on small islands Section 2.11. Absence of great apes roaming outside Africa Section 2.12. Absence of penguins in northern hemisphere Section 2.13. Absence of samarium-146 isotope from earth Section 2.14. Obligation to look for absences Glossary References 3. Drawing inferences from data ranges Section 3.1. Why are data ranges important? Section 3.2. The range of dust sizes that cause human disease Section 3.3. When tumor cells have very small nuclei Section 3.4. The range of heights that animals can jump Section 3.5. Blood chemistry Section 3.6. Narrow ranges of enzyme activity Section 3.7. The number of different types of cancers Section 3.8. Limits imposed by the dynamic range of measuring instruments Glossary References 4. Drawing inferences from outliers and exceptions Section 4.1. One is the loneliest number Section 4.2. Ozone, the outlier that couldn't be believed Section 4.3. Neoplasms having very short latency periods Section 4.4. Outliers as sentinels for common diseases Section 4.5. How exceptions elucidate pathogenesis Section 4.6. Finding the outliers Glossary References 5. What we learn when our data are abnormal Section 5.1. Creating normal distributions Section 5.2. Pareto's principle and Zipf distribution in biological systems Section 5.3. Pareto's bias: Favoring the common items Section 5.4. Recognizing composite diseases Section 5.5. Multimodality in population data Section 5.6. Removing some of the mystery around ovarian cancers Section 5.7. Living with Berkson's paradox Glossary References 6. Using time to solve cause and effect dilemmas Section 6.1. Timing is everything Section 6.2. Does anybody really know what time it is? Section 6.3. Temporal paradoxes Section 6.4. Timing the progression of cancer development Section 6.5. When the temporal sequence is observed incorrectly Section 6.6. Smoke and mirrors Section 6.7. Refusing simple answers Section 6.8. Dose-dependent effects and the fallacy of causation Section 6.9. Time-window bias Section 6.10. Replacing causation with pathogenesis Glossary References 7. Heuristic methods that use random numbers Section 7.1. The value of randomness Section 7.2. Repeated sampling Section 7.3. Monte Carlo simulations for tumor growth and metastasis Section 7.4. A seemingly unlikely string of occurrences Section 7.5. Cancer is not caused by bad luck Section 7.6. Several approaches to the birthday problem Section 7.7. Modeling cancer incidence by age Section 7.8. The Monty Hall puzzle Glossary References 8. Estimations for biomedical data Section 8.1. The inestimable value of estimates Section 8.2. The limit of hemoglobin concentration in red blood cells Section 8.3. CODIS: How to do it all without having it all Section 8.4. Some useful approximation methods Section 8.5. Some useful numbe
1. Learning what counting tells usSection 1.1. Science is mostly about counting stuff Section 1.2. Never count on an accurate count Section 1.3. Large samples cannot compensate for nonrepresentative data Section 1.4. The perils of combining data setsSection 1.5. Compositionality: Why small outnumbers large Section 1.6. Looking at data Section 1.7. Counting mutations Section 1.8. Chromosome length and the frequency of genetic diseases Section 1.9. Counting instances of species Section 1.10. Counting garbage Glossary References 2. Drawing inferences from absences of data values Section 2.1. When the important data is what you do not see Section 2.2. The power of negative thinking Section 2.3. Absence of x-rays emitted by hot cups of coffee Section 2.4. Absence of laboratory findings in SIDS (sudden infant death syndrome) Section 2.5. Absence of lethal toxicity resulting from damage to the epigenome and systems that regulate gene expressionSection 2.6. Absence of deficiency diseases among highly conserved genesSection 2.7. Absence of shared conserved noncoding elements Section 2.8. Absence of animals with built-in wheels Section 2.9. Absence of microcancers Section 2.10. Absence of frogs on small islands Section 2.11. Absence of great apes roaming outside Africa Section 2.12. Absence of penguins in northern hemisphere Section 2.13. Absence of samarium-146 isotope from earth Section 2.14. Obligation to look for absences Glossary References 3. Drawing inferences from data ranges Section 3.1. Why are data ranges important? Section 3.2. The range of dust sizes that cause human disease Section 3.3. When tumor cells have very small nuclei Section 3.4. The range of heights that animals can jump Section 3.5. Blood chemistry Section 3.6. Narrow ranges of enzyme activity Section 3.7. The number of different types of cancers Section 3.8. Limits imposed by the dynamic range of measuring instruments Glossary References 4. Drawing inferences from outliers and exceptions Section 4.1. One is the loneliest number Section 4.2. Ozone, the outlier that couldn't be believed Section 4.3. Neoplasms having very short latency periods Section 4.4. Outliers as sentinels for common diseases Section 4.5. How exceptions elucidate pathogenesis Section 4.6. Finding the outliers Glossary References 5. What we learn when our data are abnormal Section 5.1. Creating normal distributions Section 5.2. Pareto's principle and Zipf distribution in biological systems Section 5.3. Pareto's bias: Favoring the common items Section 5.4. Recognizing composite diseases Section 5.5. Multimodality in population data Section 5.6. Removing some of the mystery around ovarian cancers Section 5.7. Living with Berkson's paradox Glossary References 6. Using time to solve cause and effect dilemmas Section 6.1. Timing is everything Section 6.2. Does anybody really know what time it is? Section 6.3. Temporal paradoxes Section 6.4. Timing the progression of cancer development Section 6.5. When the temporal sequence is observed incorrectly Section 6.6. Smoke and mirrors Section 6.7. Refusing simple answers Section 6.8. Dose-dependent effects and the fallacy of causation Section 6.9. Time-window bias Section 6.10. Replacing causation with pathogenesis Glossary References 7. Heuristic methods that use random numbers Section 7.1. The value of randomness Section 7.2. Repeated sampling Section 7.3. Monte Carlo simulations for tumor growth and metastasis Section 7.4. A seemingly unlikely string of occurrences Section 7.5. Cancer is not caused by bad luck Section 7.6. Several approaches to the birthday problem Section 7.7. Modeling cancer incidence by age Section 7.8. The Monty Hall puzzle Glossary References 8. Estimations for biomedical data Section 8.1. The inestimable value of estimates Section 8.2. The limit of hemoglobin concentration in red blood cells Section 8.3. CODIS: How to do it all without having it all Section 8.4. Some useful approximation methods Section 8.5. Some useful numbe
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