Missing data are a common challenge for machine learning (ML) approaches that provide clinical decision support, especially for complex medical outcomes such as In Vitro Fertilization (IVF) treatment prediction. Imputation methods have been studied to handle missing data, and shown to provide performance improvement of prediction models. Recently, Generative Adversarial Imputation Networks (GAIN) have been successfully applied to handle missing values in clinical data. This study implements and evaluates four imputation methods: a statistical, single imputation method and three ML-based imputation approaches, being KNN, MissForest, and GAIN. Imputation was applied to the full dataset, and the resulting complete data was then used to construct three task-specific datasets: blastocyst, pregnancy, and birth. Datasets are built based on a IVF treatment database of over 300 patients, and composed of over 50 discriminative features. Prediction models are built based on a 10-fold cross validation split and five classification algorithms: MLP, Random Forest, XGBoost, Gradient Boosting, and Bayesian Logistic. After applying imputation methods, feature value distributions remained largely consistent when compared to the distributions prior to imputation. Results suggest that GAIN imputation leads to effective convergence during model training, as both generator and discriminator losses decrease considerably. GAIN imputation outperformed all other ML-based imputation methods for the three prediction tasks. While single imputation yield F1 metrics for blastocyst, pregnancy, and birth of$0.50,0.56,0.13$, GAIN yield$0.74,0.75,0.76$, respectively for the negative class; for the positive class single imputation yield F1 metrics of$0.67,0.45,0.81$, while GAIN yield$0.76,0.51,0.85$respectively. Improvement of classification model performances after imputation suggest that a generative imputation approach might be beneficial to the task at hand.

Metabolic biomarkers can act as powerful indicators of dairy cow welfare. Machine learning methods have been applied to discover discriminative biomarkers that can help predict potential risks to cow health, and consequently support early interventions to avoid decline in animal welfare and production loss. Previous studies on predictive models based on metabolic profiling have considered a limited scope of biomarkers, and have mostly been restricted to prediction of disease occurrence. This work proposes an ensemble supervised learning approach based on heterogeneous biomarker attributes obtained from dairy cow profile and history, and two metabolic profiling methods, to predict potential risks for dairy cow health, reproduction performance, and milk production loss. Best performing models are composed of either Random Forest, Multilayer Perceptropn and Extra Trees classifiers, and achieved F1 scores of 0.81, 0.86, 0.98 and 0.75 when predicting ‘Non-diseased’, ‘Bad’ or ‘Good’ reproduction performance, and ‘0’ production loss for an upcoming lactation. The source code and sample datasets for this work are made publicly available at https://github.com/bioinfoUQAM/dairy_biomarkers.

MOTIVATION: Precise identification of Biosynthetic Gene Clusters (BGCs) is a challenging task. Performance of BGC discovery tools is limited by their capacity to accurately predict components belonging to candidate BGCs, often overestimating cluster boundaries. To support optimizing the composition and boundaries of candidate BGCs, we propose reinforcement learning approach relying on protein domains and functional annotations from expert curated BGCs. RESULTS: The proposed reinforcement learning method aims to improve candidate BGCs obtained with state-of-the-art tools. It was evaluated on candidate BGCs obtained for two fungal genomes, Aspergillus niger and Aspergillus nidulans. The results highlight an improvement of the gene precision by above 15% for TOUCAN, fungiSMASH and DeepBGC; and cluster precision by above 25% for fungiSMASH and DeepBCG, allowing these tools to obtain almost perfect precision in cluster prediction. This can pave the way of optimizing current prediction of candidate BGCs in fungi, while minimizing the curation effort required by domain experts. AVAILABILITY AND IMPLEMENTATION: https://github.com/bioinfoUQAM/RL-bgc-components. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fungal Biosynthetic Gene Clusters (BGCs) of secondary metabolites are clusters of genes capable of producing natural products, compounds that play an important role in the production of a wide variety of bioactive compounds, including antibiotics and pharmaceuticals. Identifying BGCs can lead to the discovery of novel natural products to benefit human health. Previous work has been focused on developing automatic tools to support BGC discovery in plants, fungi, and bacteria. Data-driven methods, as well as probabilistic and supervised learning methods have been explored in identifying BGCs. Most methods applied to identify fungal BGCs were data-driven and presented limited scope. Supervised learning methods have been shown to perform well at identifying BGCs in bacteria, and could be well suited to perform the same task in fungi. But labeled data instances are needed to perform supervised learning. Openly accessible BGC databases contain only a very small portion of previously curated fungal BGCs. Making new fungal BGC datasets available could motivate the development of supervised learning methods for fungal BGCs and potentially improve prediction performance compared to data-driven methods. In this work we propose new publicly available fungal BGC datasets to support the BGC discovery task using supervised learning. These datasets are prepared to perform binary classification and predict candidate BGC regions in fungal genomes. In addition we analyse the performance of a well supported supervised learning tool developed to predict BGCs.

Abstract This work presents the bioMine system, a full‐text natural language search engine for biomedical literature. bioMine provides search capabilities based on the full‐text content of documents belonging to a database composed of scientific articles and allows users to submit their search queries using natural language. Beyond the text content of articles, the system engine also uses article metadata, empowering the search by considering extra information from picture and table captions. bioMine is publicly released as an open‐source system under the MIT license.

This paper presents a supervised learning approach to support the screening of HIV literature. The manual screening of biomedical literature is an important task in the process of systematic reviews. Researchers and curators have the very demanding, time-consuming and error-prone task of manually identifying documents that must be included in a systematic review concerning a specific problem. We implemented a supervised learning approach to support screening tasks, by automatically flagging potentially selected documents in a list retrieved by a literature database search. To overcome the main issues associated with the automatic literature screening task, we evaluated the use of data sampling, feature combinations, and feature selection methods, generating a total of 105 classification models. The models yielding best results were composed by the Logistic Model Trees classifier, a fairly balanced training set, and feature combination of Bag-Of-Words and MeSH terms. According to our results, the system correctly labels the great majority of relevant documents, and it could be used to support HIV systematic reviews to allow researchers to assess a greater number of documents in less time.