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Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl's work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

Probabilistic and Causal Inference (Computer Science) --- Probability learning. --- Mathematical logic. --- Artificial intelligence. --- Cognitive Science. --- Philosophy.

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Some in the social sciences argue that the same logic applies to both qualitative and quantitative methods. In A Tale of Two Cultures, Gary Goertz and James Mahoney demonstrate that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. They identify and discuss major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, Goertz and Mahoney also seek to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. This book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Social sciences --- Political sociology --- Political science --- Mass political behavior --- Political behavior --- Sociology --- Administration --- Civil government --- Commonwealth, The --- Government --- Political theory --- Political thought --- Politics --- Science, Political --- State, The --- Research --- Methodology. --- Sociological aspects --- 2 x 2 tables. --- David Hume. --- Fundamental Principle of Variable Transformation. --- Fundamental Problem of Causal Inference. --- Fundamental Tradeoffs. --- Hooke's law. --- Principle of Conceptual Opposites. --- Principle of Conceptual Overlap. --- Principle of Unimportant Variation. --- additive-linear causal model. --- aggregation technique. --- asymmetry. --- case selection. --- case studies. --- cases. --- categories. --- causal complexity. --- causal effects. --- causal heterogeneity. --- causal inference. --- causal mechanism. --- causal model. --- causal models. --- causal-process observations. --- causality. --- causation. --- cause. --- causes-of-effects approach. --- characteristics. --- concepts. --- conceptualization. --- constant conjunction definition. --- control variables. --- counterfactual analysis. --- counterfactual definition. --- counterfactuals. --- cross-case analysis. --- data analysis. --- data transformations. --- data-set observations. --- definitions. --- dependent variable. --- effects-of-causes approach. --- empirical testing. --- equifinality. --- error. --- experiments. --- fuzziness. --- fuzzy-set analysis. --- fuzzy-set transformations. --- generalization. --- hypothesis testing. --- indicators. --- individual case analysis. --- individual cases. --- inferential statistics. --- logging. --- logic. --- meaning retention. --- measurement. --- membership functions. --- methodological pluralism. --- minimum rewrite rule. --- mixed-method research. --- multimethod research. --- multiple causation. --- natural language. --- necessary condition. --- nonoccurrence. --- occurrence. --- opposites. --- perfect predictors. --- political science. --- probability theory. --- process tracing. --- qualitative research. --- quantitative research. --- regression. --- scale types. --- scope conditions. --- semantic transformations. --- semantics. --- set theory. --- set-theoretic causal model. --- set-theoretic generalization. --- social science research. --- social sciences. --- sociology. --- standardization. --- static causal asymmetry. --- statistical analysis. --- statistical method. --- statistical model. --- statistics. --- sufficient condition. --- symmetry. --- translation problems. --- typologies. --- variable transformations. --- within-case analysis. --- within-model responses.

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A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.

precision medicine informatics --- n/a --- drug sensitivity --- chromatin modification --- cell lines --- biocuration --- neurodegeneration --- multivariate analysis --- artificial intelligence --- epigenetics --- missing data --- sequencing --- clinical data --- class imbalance --- integrative analytics --- algorithm development for network integration --- deep phenotype --- non-omics data --- feature selection --- Gene Ontology --- miRNA–gene expression networks --- omics data --- plot visualization --- Alzheimer’s disease --- tissue classification --- epidemiological data --- proteomic analysis --- genotype --- RNA expression --- indirect effect --- multi-omics --- dementia --- multiomics integration --- data integration --- phenomics --- network topology analysis --- challenges --- transcriptome --- enrichment analysis --- regulatory genomics --- scalability --- heterogeneous data --- systemic lupus erythematosus --- database --- microtubule-associated protein tau --- disease variants --- genomics --- joint modeling --- distance correlation --- annotation --- phenotype --- direct effect --- curse of dimensionality --- gene–environment interactions --- logic forest --- machine learning --- KEGG pathways --- multivariate causal mediation --- amyloid-beta --- bioinformatics pipelines --- support vector machine --- pharmacogenomics --- candidate genes --- tissue-specific expressed genes --- cognitive impairment --- causal inference --- miRNA-gene expression networks --- Alzheimer's disease --- gene-environment interactions

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In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented.

Humanities --- Social interaction --- dose-escalation --- combination study --- modelling assumption --- interaction --- adaptive designs --- adaptive randomization --- Bayesian designs --- clinical trials --- predictive power --- target allocation --- Bayesian inference --- highest posterior density intervals --- normal approximation --- predictive analysis --- sample size determination --- bayesian meta-analysis --- clustering --- binary data --- priors --- frequentist validation --- Bayesian --- rare disease --- prior distribution --- meta-analysis --- sample size --- bridging studies --- distribution distance --- oncology --- phase I --- dose-finding --- dose–response --- bayesian inference --- prior elicitation --- latent dirichlet allocation --- clinical trial --- power-prior --- poor accrual --- Bayesian trial --- cisplatin --- doxorubicin --- oxaliplatin --- dose escalation --- PIPAC --- peritoneal carcinomatosis --- randomized controlled trial --- causal inference --- doubly robust estimation --- propensity score --- Bayesian monitoring --- futility rules --- interim analysis --- posterior and predictive probabilities --- stopping boundaries --- Bayesian trial design --- early phase dose finding --- treatment combinations --- optimal dose combination

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Transcriptional regulation is a critical biological process involved in the response of a cell, a tissue or an organism to a variety of intra- and extra-cellular signals. Besides, it controls the establishment and maintenance of cell identity throughout developmental and differentiation programs. This highly complex and dynamic process is orchestrated by a huge number of molecules and protein networks and occurs through multiple temporal and functional steps. Of note, many human disorders are characterized by misregulation of global transcription since most of the signaling pathways ultimately target components of transcription machinery. This book includes a selection of papers that illustrate recent advances in our understanding of transcriptional regulation and focuses on many important topics, from cis-regulatory elements to transcription factors, chromatin regulators and non-coding RNAs, other than several transcriptome studies and computational analyses.

transcription factor --- n/a --- transcription --- self-incompatibility --- cytogenetics --- epigenetics --- selenocysteine --- tea --- AP-2? --- nonsense-mediated decay --- transcriptomics --- Akt1 --- promoter --- cell metabolism --- pediveliger larvae --- Patau Syndrome --- tristetraprolin (TTP) --- long non-coding RNA (lncRNA) --- pregnancy --- G-quadruplex --- glioblastoma --- placenta --- PRDM gene family --- circRNA-disease associations --- bioadhesive --- gene expression --- Crassostrea gigas --- transcription regulation --- cell differentiation --- RNA interference --- transcriptome --- inflammatory response --- FOXO1 --- Adiponectin --- liquid chromatograph-tandem mass spectrometer (LC-MS/MS) --- selenium --- selenocysteine insertion sequence --- inflammation --- selenoproteins --- research methods --- nutritional status --- structures and functions --- CRISPR/Cas9 --- fertilization --- melanin --- differentially expressed genes --- tyrosinase --- posttranscriptional regulation --- major depressive disorder --- human malignancies --- pathway --- CDKN1C --- transcription factors --- p57Kip2 --- enhancer activity --- mouse --- disorders --- high-throughput RNA sequencing (RNA-Seq) --- TCGA data analysis --- RNA-seq --- heterogeneous network --- insect --- and drug design --- therapeutic targets --- mechanisms --- obesity --- Pacific oyster --- Rsh regulon --- common pathway --- Pax3 --- somatic mutations --- nutrition --- molecular docking --- bioinformatics --- interactome --- long non-coding RNAs --- transcriptional regulation --- Pteria penguin (Röding --- Adiponectin receptors --- transcriptome profiling --- 1798) --- N-acyl-l-homoserine lactone --- ppGpp --- tumorigenesis --- sphingomonads --- human --- disease --- adenosine and uridine-rich elements (AREs) --- progress and prospects --- miR-25-3p --- acute leukemia --- Novosphingobium pentaromativorans US6-1 --- microscopy --- cancer --- molecular pathways --- causal inference --- Pteria penguin (Röding