Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. METHODS: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). RESULTS: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. CONCLUSIONS: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification.

With the development of new sequencing technologies, availability of genomic data has grown exponentially. Over the past decade, numerous studies have used genomic data to identify associations between genes and biological functions. While these studies have shown success in annotating genes with functions, they often assume that genes are completely annotated and fail to take into account that datasets are sparse and noisy. This work proposes a method to detect missing annotations in the context of hierarchical multi-label classification. More precisely, our method exploits the relations of functions, represented as a hierarchy, by computing probabilities based on the paths of functions in the hierarchy. By performing several experiments on a variety of rice (Oriza sativa Japonica), we showcase that the proposed method accurately detects missing annotations and yields superior results when compared to state-of-art methods from the literature.

Online Extra Trees Regressor.

Data production has followed an increased growth in the last years, to the point that traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of generated data. Stream or online ML presents itself as a viable solution to deal with the dynamic nature of streaming data. Besides coping with the inherent challenges of streaming data, online ML solutions must be accurate, fast, and bear a reduced memory footprint. We propose a new decision tree-based ensemble algorithm for online ML regression named online extra trees (OXT). Our proposal takes inspiration from the batch learning extra trees (XT) algorithm, a popular and faster alternative to random forest (RF). While speed and memory costs might not be a central concern in most batch applications, they become crucial in data stream data learning. Our proposal combines subbagging (sampling without replacement), random tree split points, and model trees to deliver competitive prediction errors and reduced computational costs. Throughout an extensive experimental evaluation comprising 22 real-world and synthetic datasets, we compare OXT against the state-of-the-art adaptive RF (ARF) and other incremental regressors. OXT is generally more accurate than its competitors while running significantly faster than ARF and expending significantly less memory.

Machine learning for discovering missing or wrong protein function annotations : A comparison using updated benchmark datasets.

BACKGROUND: A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein functions. More specifically, many studies have investigated hierarchical multi-label classification (HMC) methods to predict annotations, using the Functional Catalogue (FunCat) or Gene Ontology (GO) label hierarchies. Most of these studies employed benchmark datasets created more than a decade ago, and thus train their models on outdated information. In this work, we provide an updated version of these datasets. By querying recent versions of FunCat and GO yeast annotations, we provide 24 new datasets in total. We compare four HMC methods, providing baseline results for the new datasets. Furthermore, we also evaluate whether the predictive models are able to discover new or wrong annotations, by training them on the old data and evaluating their results against the most recent information. RESULTS: The results demonstrated that the method based on predictive clustering trees, Clus-Ensemble, proposed in 2008, achieved superior results compared to more recent methods on the standard evaluation task. For the discovery of new knowledge, Clus-Ensemble performed better when discovering new annotations in the FunCat taxonomy, whereas hierarchical multi-label classification with genetic algorithm (HMC-GA), a method based on genetic algorithms, was overall superior when detecting annotations that were removed. In the GO datasets, Clus-Ensemble once again had the upper hand when discovering new annotations, HMC-GA performed better for detecting removed annotations. However, in this evaluation, there were less significant differences among the methods. CONCLUSIONS: The experiments have showed that protein function prediction is a very challenging task which should be further investigated. We believe that the baseline results associated with the updated datasets provided in this work should be considered as guidelines for future studies, nonetheless the old versions of the datasets should not be disregarded since other tasks in machine learning could benefit from them.