Prediction of Natural Product Classes Using Machine Learning and 13C NMR Spectroscopic Data.

Structure elucidation of chemical compounds is a complex and challenging activity that requires expertise and well-suited tools. To assign the molecular structure of a given compound, 13C NMR is one of the most widely used techniques because of its broad range of structural information. Taking into account that molecules found in nature can be grouped into natural product (NP) classes because of structural similarities, we explore the possibility of NP class prediction via 13C NMR data. Employing freely available 13C NMR data of NPs, we trained four classifiers for the prediction of eight common NP classes. The best performance was obtained with the XGBoost classifier reaching f1-scores of above 0.82. We also performed experiments with different percentages of positive samples, including the glycoside presence. Furthermore, we tested cases outside the data set, yielding performances above 80% for most classes. For the chromans case, we restricted the test examples to the coumarin subclass, and the prediction accuracy increased to 100%.

Chagas parasite detection in blood images using AdaBoost.

The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM) is also provided. Our experimental work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods.

Composition of web services using Markov decision processes and dynamic programming.

We propose a Markov decision process model for solving the Web service composition (WSC) problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate our approach, with artificial and real data. The experimental results show the reliability of the model and the methods employed, with policy iteration being the best one in terms of the minimum number of iterations needed to estimate an optimal policy, with the highest Quality of Service attributes. Our experimental work shows how the solution of a WSC problem involving a set of 100,000 individual Web services and where a valid composition requiring the selection of 1,000 services from the available set can be computed in the worst case in less than 200 seconds, using an Intel Core i5 computer with 6 GB RAM. Moreover, a real WSC problem involving only 7 individual Web services requires less than 0.08 seconds, using the same computational power. Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity.

An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images.

Chagas disease is a tropical parasitic disease caused by the flagellate protozoan Trypanosoma cruzi (T. cruzi) and currently affecting large portions of the Americas. One of the standard laboratory methods to determine the presence of the parasite is by direct visualization in blood smears stained with some colorant. This method is time-consuming, requires trained microscopists and is prone to human mistakes. In this article we propose a novel algorithm for the automatic detection of T. cruzi parasites, in microscope digital images obtained from peripheral blood smears treated with Wright's stain. Our algorithm achieved a sensitivity of 0.98 and specificity of 0.85 when evaluated against a dataset of 120 test images. Experimental results show the versatility of the method for parasitemia determination.