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Do our genes determine our behavior? Do humans occupy a unique position in evolution? To clarify these provoking questions, the author takes the reader on an ambitious and entertaining journey through a variety of scientific disciplines. In doing so, he creates an image of human evolution that argues that our entire individual knowledge is determined - to the smallest detail - by phylogeny. A provoking and controversial analysis of the theory of our inability to learn something new and of the extent to which our behavior is determined by our genes.
Humanity's physical design flaws have long been apparent--we get hemorrhoids and impacted wisdom teeth, for instance--but do the imperfections extend down to the level of our genes? Inside the Human Genome is the first book to examine the philosophical question of why, from the perspectives of biochemistry and molecular genetics, flaws exist in the biological world. Distinguished evolutionary geneticist John Avise offers a panoramic yet penetrating exploration of the many gross deficiencies in human DNA--ranging from mutational defects to built-in design faults--while at the same time offering a comprehensive treatment of recent findings about the human genome. The author shows that the overwhelming scientific evidence for genomic imperfection provides a compelling counterargument to intelligent design. He also develops a case that theologians should welcome rather than disavow these discoveries. The evolutionary sciences can help mainstream religions escape the shackles of Intelligent Design, and thereby return religion to its rightful realm--not as the secular interpreter of the biological minutiae of our physical existence, but rather as a respectable philosophical counselor on grander matters of ultimate concern.
A scientific response to the best-selling The Bell Curve which set off a hailstorm of controversy upon its publication in 1994. Much of the public reaction to the book was polemic and failed to analyse the details of the science and validity of the statistical arguments underlying the books conclusion. Here, at last, social scientists and statisticians reply to The Bell Curve and its conclusions about IQ, genetics and social outcomes.
Science need not be dull and bogged down by jargon, as Richard Dawkins proves in this entertaining look at evolution. The themes he takes up are the concepts of altruistic and selfish behaviour; the genetical definition of selfish interest; the evolution of aggressive behaviour; kinshiptheory; sex ratio theory; reciprocal altruism; deceit; and the natural selection of sex differences. 'Should be read, can be read by almost anyone. It describes with great skill a new face of the theory of evolution.' W.D. Hamilton, Science
Genomic science indicates that humans descend not from an individual pair but from a large population. What does this mean for the basic claim of many Christians: that humans descend from Adam and Eve? Leading evangelical geneticist Dennis Venema and popular New Testament scholar Scot McKnight combine their expertise to offer informed guidance and answers to questions pertaining to evolution, genomic science, and the historical Adam. Some of the questions they explore include: - Is there credible evidence for evolution? - Do we descend from a population or are we the offspring of Adam and Eve? - Does taking the Bible seriously mean rejecting recent genomic science? - How do Genesis's creation stories reflect their ancient Near Eastern context, and how did Judaism understand the Adam and Eve of Genesis? - Doesn't Paul's use of Adam in the New Testament prove that Adam was a historical individual? The authors address up-to-date genomics data with expert commentary from both genetic and theological perspectives, showing that genome research and Scripture are not irreconcilable. Foreword by Tremper Longman III and afterword by Daniel Harrell.
A top behavioral geneticist argues DNA inherited from our parents at conception can predict our psychological strengths and weaknesses. This “modern classic” on genetics and nature vs. nurture is “one of the most direct and unapologetic takes on the topic ever written” (Boston Review). In Blueprint, behavioral geneticist Robert Plomin describes how the DNA revolution has made DNA personal by giving us the power to predict our psychological strengths and weaknesses from birth. A century of genetic research shows that DNA differences inherited from our parents are the consistent lifelong sources of our psychological individuality—the blueprint that makes us who we are. Plomin reports that genetics explains more about the psychological differences among people than all other factors combined. Nature, not nurture, is what makes us who we are. Plomin explores the implications of these findings, drawing some provocative conclusions—among them that parenting styles don't really affect children's outcomes once genetics is taken into effect. This book offers readers a unique insider’s view of the exciting synergies that came from combining genetics and psychology.
"This book attempts to make a comprehensive, interdisciplinary case for a new view of the origin of life"--Prologue.
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
This book describes the basic ideas of gene expression programming (GEP) and numerous modifications to this powerful new algorithm. It provides all the implementation details of GEP so that anyone with elementary programming skills will be able to implement it themselves. The book includes a self-contained introduction to this new exciting field of computational intelligence. This second edition has been revised and extended with five new chapters.
These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.