Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

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Modern Applied Statistics With S

http://www.tinread.usarb.md:8888/tinread/fulltext/lal/soil_structure.pdf

Soil structure and management: a review

Genetics and analysis of quantitative traits

Real and Complex Analysis

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DMU - A Package for Analyzing Multivariate Mixed Models in Quantitative Genetics and Genomics

DMU is a package directed towards applications in quantitative genetics, and it implements powerful tools to estimate variance components and fixed effects (BLUE) and to predict random effects (BLUP). Most of the development of DMU has been in response to the needs in research projects in applied quantitative animal genetics along a period extending for more than 25 years. Indeed, DMU has been the main tool for statistical inference (both estimation and prediction) in the Danish animal breeding research. Moreover, DMU has been used in routine genetic evaluation of cattle, sheep, mink and horse in Denmark, and in several other countries. This led to a package implementing an ample range of statistical methods and computation algorithms, which not only implement high performance methods for specific project-related problems, but also are of general applicability in quantitative genetics.

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DMU - A Package for analyzing multivariate mixed models

Background Schizoaffective disorder may be related to both schizophrenia and bipolar disorders, but no population-based studies, to our knowledge, have investigated this association in families. Objectives To determine whether a psychiatric history of schizoaffective disorder, bipolar disorder, or schizophrenia among parents and siblings is a risk factor for developing a schizoaffective disorder, and whether a specific pattern of family history of psychiatric illness exists in persons with schizoaffective disorder compared with persons with bipolar disorder or schizophrenia. Design Register-based cohort study. Setting Denmark. Cohort The 2.4 million persons born in Denmark after 1952. Main Outcome Measures Relative risks of the 3 illnesses estimated by Poisson regression. Results In total, 1925 persons had a schizoaffective disorder, 3721 had a bipolar disorder, and 12 501 had schizophrenia. The relative risk of schizoaffective disorder was 2.76 (95% confidence interval, 2.49-3.06) if a first-degree relative had a history of mental illness compared with a person with no first-degree relatives with such a history. There was an additional risk (95% confidence interval) of 2.57 (2.11-3.13), 3.23 (2.63-3.95), or 1.92 (1.43-2.57) if the first-degree relative had schizophrenia, bipolar disorder, or schizoaffective disorder, respectively, compared with other psychiatric admissions. When bipolar disorder was the outcome, bipolar disorder in first-degree relatives was by far the significantly strongest risk factor. When schizophrenia was the outcome, the significantly strongest risk factor was schizophrenia among first-degree relatives. Conclusion Schizoaffective disorder is not simply a subgroup of either bipolar disorder or schizophrenia but may be genetically linked to both, with schizoaffective disorder being a subtype of each or a genetic intermediate form.

https://jamanetwork.com/journals/jamapsychiatry/articlepdf/208840/yoa40162.pdf

Family History of Psychiatric Illness as a Risk Factor for Schizoaffective Disorder: A Danish Register-Based Cohort Study

Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example Kruskal’s algorithm. The efficiency of the algorithm makes it tractable for high-dimensional problems. Results: We extend Chow and Liu’s approach in two ways: first, to find the forest optimizing a penalized likelihood criterion, for example AIC or BIC, and second, to handle data with both discrete and Gaussian variables. We apply the approach to three datasets: two from gene expression studies and the third from a genetics of gene expression study. The minimal BIC forest supplements a conventional analysis of differential expression by providing a tentative network for the differentially expressed genes. In the genetics of gene expression context the method identifies a network approximating the joint distribution of the DNA markers and the gene expression levels. Conclusions: The approach is generally useful as a preliminary step towards understanding the overall dependence structure of high-dimensional discrete and/or continuous data. Trees and forests are unrealistically simple models for biological systems, but can provide useful insights. Uses include the following: identification of distinct connected components, which can be analysed separately (dimension reduction); identification of neighbourhoods for more detailed analyses; as initial models for search algorithms with a larger search space, for example decomposable models or Bayesian networks; and identification of interesting features, such as hub nodes.

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Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests.

We present an attractive new system for the specific and sensitive detection of the malaria-causing Plasmodium parasites. The system relies on isothermal conversion of single DNA cleavage–ligation events catalyzed specifically by the Plasmodium enzyme topoisomerase I to micrometer-sized products detectable at the single-molecule level. Combined with a droplet microfluidics lab-on-a-chip platform, this design allowed for sensitive, specific, and quantitative detection of all human-malaria-causing Plasmodium species in single drops of unprocessed blood with a detection limit of less than one parasite/μL. Moreover, the setup allowed for detection of Plasmodium parasites in noninvasive saliva samples from infected patients. During recent years malaria transmission has declined worldwide, and with this the number of patients with low-parasite density has increased. Consequently, the need for accurate detection of even a few parasites is becoming increasingly important for the continued combat against the disea...

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Droplet Microfluidics Platform for Highly Sensitive and Quantitative Detection of Malaria-Causing Plasmodium Parasites Based on Enzyme Activity Measurement

Rodrigo Labouriau

Papers

DMU - A Package for Analyzing Multivariate Mixed Models in Quantitative Genetics and Genomics

DMU - A Package for analyzing multivariate mixed models

Family History of Psychiatric Illness as a Risk Factor for Schizoaffective Disorder: A Danish Register-Based Cohort Study

Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests.

Droplet Microfluidics Platform for Highly Sensitive and Quantitative Detection of Malaria-Causing Plasmodium Parasites Based on Enzyme Activity Measurement