Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.

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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

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Principal component analysis

The aim of this work was the calibration and validation of mathematical models based on a quantitative structure–activity relationship approach to discriminate sweet, tasteless and bitter molecules. The sweet-tasteless and the sweet-bitter datasets included 566 and 508 compounds, respectively. A total of 3763 conformation-independent Dragon molecular descriptors were calculated and subsequently reduced through both unsupervised reduction and supervised selection coupled with the k-nearest neighbors classification technique. A model based on nine descriptors was retained as the optimal one for sweet and tasteless molecules, while a model based on four descriptors was calibrated for the sweetness-bitterness dataset. Models were properly validated through cross-validation and external test sets. The applicability domain of models was investigated, and the interpretation of the role of the molecular descriptors in classifying sweet and non-sweet tastes was evaluated. The classification and the performance of the models presented in this paper are simple but accurate. They are based on a relatively small number of descriptors and a straightforward classification approach. The results presented here indicate that the proposed models can be used to accurately select new compounds as potential sweetener candidates.

Quantitative structure–activity relationships to predict sweet and non-sweet tastes

Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.

Molecular descriptors for structure–activity applications: A hands-on approach

Quantitative Structure - Activity Relationship (QSAR) models play a central role in medicinal chemistry, toxicology and computer-assisted molecular design, as well as a support for regulatory decisions and animal testing reduction. Thus, assessing their predictive ability becomes an essential step for any prospective application. Many metrics have been proposed to estimate the model predictive ability of QSARs, which have created confusion on how models should be evaluated and properly compared. Recently, we showed that the metric Q F 3 2 is particularly well-suited for comparing the external predictivity of different models developed on the same training dataset. However, when comparing models developed on different training data, this function becomes inadequate and only dispersion measures like the root-mean-square error (RMSE) should be used. The intent of this work is to provide clarity on the correct and incorrect uses of Q F 3 2 , discussing its behavior towards the training data distribution and illustrating some cases in which Q F 3 2 estimates may be misleading. Hereby, we encourage the usage of measures of dispersions when models trained on different datasets have to be compared and evaluated.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/minf.201800029

On the Misleading Use of QF32 for QSAR Model Comparison

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications.

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modelled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.

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Davide Ballabio

Papers

Quantitative structure–activity relationships to predict sweet and non-sweet tastes

Molecular descriptors for structure–activity applications: A hands-on approach

On the Misleading Use of QF32 for QSAR Model Comparison

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications.

A QSTR-based expert system to predict sweetness of molecules