Abstract. In order to reduce human efforts, there has been increasing interest in applying active learning for training text classifiers. This paper describes a straightforward active learning heuristic, representative sampling, which explores the clustering structure of 'uncertain' documents and identifies the representative samples to query the user opinions, for the purpose of speeding up the convergence of Support Vector Machine (SVM) classifiers. Compared with other active learning algorithms, the proposed representative sampling explicitly addresses the problem of selecting more than one unlabeled documents. In an empirical study we compared representative sampling both with random sampling and with SVM active learning. The results demonstrated that representative sampling offers excellent learning performance with fewer labeled documents and thus can reduce human efforts in text classification tasks.

The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance.

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BackgroundThe Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive.MethodIn this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the drug labeling with a goal of discovering “topics” that group drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved drug labels were used in this study. First, the three labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of drugs.ResultsThe results demonstrate that drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific medications via topics.ConclusionsThe successful application of topic modeling on the FDA drug labeling demonstrates its potential utility as a hypothesis generation means to infer hidden relationships of concepts such as, in this study, drug safety and therapeutic use in the study of biomedical documents.

BackgroundDrug repositioning offers an opportunity to revitalize the slowing drug discovery pipeline by finding new uses for currently existing drugs. Our hypothesis is that drugs sharing similar side effect profiles are likely to be effective for the same disease, and thus repositioning opportunities can be identified by finding drug pairs with similar side effects documented in U.S. Food and Drug Administration (FDA) approved drug labels. The safety information in the drug labels is usually obtained in the clinical trial and augmented with the observations in the post-market use of the drug. Therefore, our drug repositioning approach can take the advantage of more comprehensive safety information comparing with conventional de novo approach.MethodA probabilistic topic model was constructed based on the terms in the Medical Dictionary for Regulatory Activities (MedDRA) that appeared in the Boxed Warning, Warnings and Precautions, and Adverse Reactions sections of the labels of 870 drugs. Fifty-two unique topics, each containing a set of terms, were identified by using topic modeling. The resulting probabilistic topic associations were used to measure the distance (similarity) between drugs. The success of the proposed model was evaluated by comparing a drug and its nearest neighbor (i.e., a drug pair) for common indications found in the Indications and Usage Section of the drug labels.ResultsGiven a drug with more than three indications, the model yielded a 75% recall, meaning 75% of drug pairs shared one or more common indications. This is significantly higher than the 22% recall rate achieved by random selection. Additionally, the recall rate grows rapidly as the number of drug indications increases and reaches 84% for drugs with 11 indications. The analysis also demonstrated that 65 drugs with a Boxed Warning, which indicates significant risk of serious and possibly life-threatening adverse effects, might be replaced with safer alternatives that do not have a Boxed Warning. In addition, we identified two therapeutic groups of drugs (Musculo-skeletal system and Anti-infective for systemic use) where over 80% of the drugs have a potential replacement with high significance.ConclusionTopic modeling can be a powerful tool for the identification of repositioning opportunities by examining the adverse event terms in FDA approved drug labels. The proposed framework not only suggests drugs that can be repurposed, but also provides insight into the safety of repositioned drugs.

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Rhizoma drynariae is the main traditional Chinese medicine used for the treatment of osteoporosis, but its anti-osteoporotic targeting mechanism has not been fully elucidated due to the complexity of its active ingredients. In this study, the pharmacological mechanism of action of Rhizoma drynariae against osteoporosis was studied by integrating pharmacological concepts. The pharmacokinetic characteristics of selected major active constituents of Rhizoma drynariae and the SMILES structural similarity were used to predict related targets. A literature search was conducted to identify known osteoporosis treatment targets, which were then combined with the predicted targets to construct the direct or indirect target interaction network map of Rhizoma drynariae against osteoporosis. Finally, data on the key targets of the interactions, ranked according to relevant node parameters obtained through pathway enrichment analysis and screening of key targets and active ingredients of Rhizoma drynariae , were used to perform molecular docking simulation. We screened 16 active ingredients of Rhizoma drynariae , and 7 key targets with direct or indirect effects with a high frequency were obtained. These main pathways were found to play important roles in the PI3k-akt signaling pathway, osteoclast differentiation, Wnt signaling pathway, and estrogen signaling pathway. Molecular docking showed that most active ingredients of Rhizoma drynariae had strong binding efficiency with key targets. Based on network pharmacology, we conclude that Rhizoma drynariae plays an anti-osteoporotic role by directly or indirectly targeting multiple major signaling pathways and influencing the proliferation and differentiation of multiple types of cells.

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