Bipartite learning is a machine learning task that aims to predict interactions between pairs of instances. It has been applied to various domains, including drug-target interactions, RNA-disease associations, and regulatory network inference. Despite being widely investigated, current methods still present drawbacks, as they are often designed for a specific application and thus do not generalize to other problems or present scalability issues. To address these challenges, we propose Oxytrees: proxy-based biclustering model trees. Oxytrees compress the interaction matrix into row- and column-wise proxy matrices, significantly reducing training time without compromising predictive performance. We also propose a new leaf-assignment algorithm that significantly reduces the time taken for prediction. Finally, Oxytrees employ linear models using the Kronecker product kernel in their leaves, resulting in shallower trees and thus even faster training. Using 15 datasets, we compared the predictive performance of ensembles of Oxytrees with that of the current state-of-the-art. We achieved up to 30-fold improvement in training times compared to state-of-the-art biclustering forests, while demonstrating competitive or superior performance in most evaluation settings, particularly in the inductive setting. Finally, we provide an intuitive Python API to access all datasets, methods and evaluation measures used in this work, thus enabling reproducible research in this field.

Long non-coding RNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases, such as neurological diseases, cardiovascular diseases, AIDS, diabetes, and various types of cancer. In the machine learning literature, lncRNA-disease association (LDA) has been widely investigated as a binary classification problem, where each lncRNA-disease pair is seen as an independent instance. This approach presents drawbacks as it does not exploit the correlation among the diseases, aggravates the already imbalanced dataset, and substantially increases the execution time. Furthermore, the literature focuses on the transductive setting where new disease associations are predicted in lncRNAs already seen by the model, which naturally restricts its application to already seen lncRNAs. As a solution, we propose to address LDA prediction as a structured output prediction problem, namely (hierarchical) multi-label classification, where all LDAs are predicted at once for a given lncRNA. We compared several LDA methods and their structured output variants with recent (hierarchical) multi-label classification methods in an inductive setting, e.g., disease associations are predicted in unseen lncRNAs. Our experiments reveal that approaching LDA prediction with structured output prediction leads to superior or competitive results while drastically reducing the running time.

Supervised machine learning algorithms usually require sufficient labeled data to perform well. However, obtaining this information can be challenging due to monetary and time constraints. As a possible solution, recent works have proposed the combination of active and semi-supervised learning techniques. Active semi-supervised learning investigates methods to efficiently construct predictive models by incorporating unlabeled data, which is either labeled by a domain expert or pseudolabeled by a model. Despite already being studied in other problems, to the best of our knowledge, active semi-supervised learning has not been applied in the context of multi-target regression, a predictive task where multiple continuous targets must be predicted. In this work, we investigate active semi-supervised learning for multi-target regression. More specifically, we propose, MASSTER, Multi-target Active Semi-Supervised Training for Regression, a novel ensemble method that identifies the most relevant instance-target pairs based on the variance in their predictions. Experiments using 8 benchmark datasets reveal that our method for active learning provides superior results in most of the cases when compared to the current state-of-the-art active learning method for multi-target regression. Further, as its semi-supervised component, our method incorporates a variation of both self-learning (MASSTER-SL) and co-training (MASSTER-CT). Both variants presented better metrics in earlier epochs when compared to MASSTER-AL version with less labeled data especially in smaller datasets.

Due to recent technological advances, a massive amount of data is generated on a daily basis. Unfortunately, this is not always beneficial as such data may present weak-supervision, meaning that the output space can be incomplete, inexact, and inaccurate. This kind of problems is investigated in weakly-supervised learning. In this work, we explore weak-label learning, a structured output prediction task for weakly-supervised problems where positive annotations are reliable, whereas negatives are missing. For the first time in this class of problems, we investigate deep forest algorithms based on tree-embeddings, a recently proposed feature representation strategy leveraging the structure of decision trees. Furthermore, we propose two new procedures for label-imputation in each layer, named Strict Label Complement (SLC), which provides fixed conservative estimates for the number of missing labels and employs them to restrict imputations, and Fluid Label Addition (FLA), which performs such estimations on every layer and uses them to adjust the imputer’s predicted probabilities without any restrictions. We combine the new approaches with deep forest architectures to produce four new algorithms: SLCForest and FLAForest, using output space feature augmentation, and also the cascade forest embedders CaFE-SLC and CaFE-FLA, employing both tree-embeddings and the output space. Our results reveal that our methods provide superior or competitive performance to the state-of-the-art. Furthermore, we also noticed that our methods are associated with better results even in cases without weak-supervision.

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.

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.

Transposable Elements (TEs) are DNA sequences capable of moving within a cell's genome. Their transposition has many effects in genomes, such as creating genetic variability and promoting changes in genes' functionality. Recently, TEs classification has been addressed using Machine Learning (ML), more specifically by Hierarchical Classification (HC) methods. Such works proved to be superior than previous ones in the literature. However, there is still room for improvement performance wise. In this direction, Deep Neural Networks (DNNs) have attracted a lot of attention in ML. In particular, Stacked Denoising Auto-Encoders (DAEs) and Deep Multi Layer-Perceptrons (MLPs) are known to provide outstanding results. By performing an extensive evaluation, our results point out that DNNs can enhance the performance of HC methods, being able to push further the state-of-art in TEs' classification.

Hierarchical Classification (HC) consists of classification problems whose classes are structured in a hierarchical fashion. Many problems are addressed by HC, in special a decent amount of works dealt with bioinformatics related problems such as Protein Function Prediction (PFP) and Transposable Elements (TEs) classification. Both of them are still a challenging task for HC due to the noisy and imbalanced nature of the datasets. As a countermeasure, Stacking is an ensemble method capable of generalizing knowledge from many classifiers. In this work, we propose three Stacking methods for HC and evaluate its performance on PFP and TEs datasets. Our results show that, when compared to regular Stacking and state-of-art methods from the literature, our methods are able to obtain superior or competitive performances.

Transposable Elements are DNA sequences that can move from one place to another inside the genome of a cell. They are important for genetic variability, and can modify the functionality of genes. The correct classification of these elements is crucial to understand their role in the evolution of species. In this paper, we investigate Transposable Elements classification as a Hierarchical Classification problem using Machine Learning. We present new hierarchical datasets suitable to be used by Machine Learning methods, and also new hierarchical top-down classification strategies using neural networks. We compared our strategies with existing ones in the literature, and evaluated them using measures specific for hierarchical problems. Experiments showed that our proposal achieved better or competitive results than those found by other methods in the literature.