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Logic and Critical Thinking in the Biomedical Sciences - 1st Edition - ISBN: 9780128213698, 9780128213629

Logic and Critical Thinking in the Biomedical Sciences

1st Edition

Volume 2: Deductions Based Upon Quantitative Data

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Author: Jules Berman
Paperback ISBN: 9780128213698
eBook ISBN: 9780128213629
Imprint: Academic Press
Published Date: 9th July 2020
Page Count: 290
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Description

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.

Key Features

Demonstrates that a great deal can be deduced from quantitative data, without applying any statistical or mathematical analyses
Provides readers with simple techniques for quickly reviewing and finding important relationships hidden within large and complex sets of data
Using examples drawn from the biomedical literature, discusses common pitfalls in data interpretation and how they can be avoided

Readership

Bioinformaticians; biostatisticians; graduate students; medical students. Members of biomedical field in general who deal with data

Table of Contents

1. Learning what counting tells us
Section 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 sets
Section 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 expression
Section 2.6. Absence of deficiency diseases among highly conserved genes
Section 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 numbers
Glossary
References

Details

No. of pages:
290
Language:
English
Copyright:
© Academic Press 2020
Published:
9th July 2020
Imprint:
Academic Press
Paperback ISBN:
9780128213698
eBook ISBN:
9780128213629

About the Author

Jules Berman

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)

Affiliations and Expertise

Author with expertise in informatics, computer programming, and cancer biology

Ratings and Reviews