Knowledge Discovery in Multi-label Phenotype Data

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

Accurate and Efficient Large-Scale Multi-Label Learning With Reduced Feature Broad Learning System Using Label Correlation

Multi-label learning for large-scale data is a grand challenge because of a large number of labels with a complex data structure. Hence, the existing large-scale multi-label methods either have unsatisfactory classification performance or are extremely time-consuming for training utilizing a massive amount of data. A broad learning system (BLS), a flat network with the advantages of succinct structures, is appropriate for addressing large-scale tasks. However, existing BLS models are not directly applicable for large-scale multi-label learning due to the large and complex label space. In this work, a novel multi-label classifier based on BLS (called BLS-MLL) is proposed with two new mechanisms: kernel-based feature reduction module and correlation-based label thresholding. The kernel-based feature reduction module contains three layers, namely, the feature mapping layer, enhancement nodes layer, and feature reduction layer. The feature mapping layer employs elastic network regularization to solve the randomness of features in order to improve performance. In the enhancement nodes layer, the kernel method is applied for high-dimensional nonlinear conversion to achieve high efficiency. The newly constructed feature reduction layer is used to further significantly improve both the training efficiency and accuracy when facing high-dimensionality with abundant or noisy information embedded in large-scale data. The correlation-based label thresholding enables BLS-MLL to generate a label-thresholding function for effective conversion of the final decision values to logical outputs, thus, improving the classification performance. Finally, experimental comparisons among six state-of-the-art multi-label classifiers on ten datasets demonstrate the effectiveness of the proposed BLS-MLL. The results of the classification performance show that BLS-MLL outperforms the compared algorithms in 86% of cases with better training efficiency in 90% of cases.

Applications of Depth Minimization of Decision Trees Containing Hypotheses for Multiple-Value Decision Tables

In this research, we consider decision trees that incorporate standard queries with one feature per query as well as hypotheses consisting of all features' values. These decision trees are used to represent knowledge and are comparable to those investigated in exact learning, in which membership queries and equivalence queries are used. As an application, we look into the issue of creating decision trees for two cases: the sorting of a sequence that contains equal elements and multiple-value decision tables which are modified from UCI Machine Learning Repository. We contrast the efficiency of several forms of optimal (considering the parameter depth) decision trees with hypotheses for the aforementioned applications. We also investigate the efficiency of decision trees built by dynamic programming and by an entropy-based greedy method. We discovered that the greedy algorithm produces very similar results compared to the results of dynamic programming algorithms. Therefore, since the dynamic programming algorithms take a long time, we may readily apply the greedy algorithms.

Inaccurate-Supervised Learning With Generative Adversarial Nets

Inaccurate-supervised learning (ISL) is a weakly supervised learning framework for imprecise annotation, which is derived from some specific popular learning frameworks, mainly including partial label learning (PLL), partial multilabel learning (PML), and multiview PML (MVPML). While PLL, PML, and MVPML are each solved as independent models through different methods and no general framework can currently be applied to these frameworks, most existing methods for solving them were designed based on traditional machine-learning techniques, such as logistic regression, KNN, SVM, decision tree. Prior to this study, there was no single general framework that used adversarial networks to solve ISL problems. To narrow this gap, this study proposed an adversarial network structure to solve ISL problems, called ISL with generative adversarial nets (ISL-GANs). In ISL-GAN, fake samples, which are quite similar to real samples, gradually promote the Discriminator to disambiguate the noise labels of real samples. We also provide theoretical analyses for ISL-GAN in effectively handling ISL data. In this article, we propose a general framework to solve PLL, PML, and MVPML, while in the published conference version, we adopt the specific framework, which is a special case of the general one, to solve the PLL problem. Finally, the effectiveness is demonstrated through extensive experiments on various imprecise annotation learning tasks, including PLL, PML, and MVPML.

Predicting odor from molecular structure: a multi-label classification approach

Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure–odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time.

Multilabel Text Classification Algorithm Based on Fusion of Two-Stream Transformer

Existing multilabel text classification methods rely on a complex manual design to mine label correlation, which has the risk of overfitting and ignores the relationship between text and labels. To solve the above problems, this paper proposes a multilabel text classification algorithm based on a transformer encoder–decoder, which can adaptively extract the dependency relationship between different labels and text. First, text representation learning is carried out through word embedding and a bidirectional long short-term memory network. Second, the global relationship of the text is modeled by the transformer encoder, and then the multilabel query is adaptively learned by the transformer decoder. Last, a weighted fusion strategy under the supervision of multiple loss functions is proposed to further improve the classification performance. The experimental results on the AAPD and RCV1-V2 datasets show that compared with the existing methods, the proposed algorithm achieves better classification results. The optimal micro-F1 reaches 73.4% and 87.8%, respectively, demonstrating the effectiveness of the proposed algorithm.

Research on Multi-label Text Classification Method Based on tALBERT-CNN

Single-label classification technology has difficulty meeting the needs of text classification, and multi-label text classification has become an important research issue in natural language processing (NLP). Extracting semantic features from different levels and granularities of text is a basic and key task in multi-label text classification research. A topic model is an effective method for the automatic organization and induction of text information. It can reveal the latent semantics of documents and analyze the topics contained in massive information. Therefore, this paper proposes a multi-label text classification method based on tALBERT-CNN: an LDA topic model and ALBERT model are used to obtain the topic vector and semantic context vector of each word (document), a certain fusion mechanism is adopted to obtain in-depth topic and semantic representations of the document, and the multi-label features of the text are extracted through the TextCNN model to train a multi-label classifier. The experimental results obtained on standard datasets show that the proposed method can extract multi-label features from documents, and its performance is better than that of the existing state-of-the-art multi-label text classification algorithms.

Mutual information-based multi-output tree learning algorithm

A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning algorithm that consists of variable selection and split optimization. The proposed method discretizes each variable based on k-means into 2–4 clusters and selects the variable for splitting based on the discretized variables using mutual information. This variable selection component has relatively low time complexity and can be applied regardless of output dimension and types. The proposed split optimization component is more efficient than an exhaustive search. The performance of the proposed tree learning algorithm is similar to or better than that of a multi-output version of CART algorithm on a specific dataset. In addition, with a large dataset, the time complexity of the proposed algorithm is significantly reduced compared to a CART algorithm.

A lazy feature selection method for multi-label classification

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

MLCE: A Multi-Label Crotch Ensemble Method for Multi-Label Classification

Multi-label classification addresses the problem that each instance is associated with multiple labels simultaneously. In this paper, we propose a multi-label crotch ensemble (MLCE) model for multi-label classification, which takes label correlations into consideration. In MLCE, a multi-label cluster tree is first constructed. Then, we incorporate all multi-label crotch predictors of the tree into a classifier, where the multi-label crotch predictor is the crotch formed by an inner node of the tree and its children. Finally, a flexible weighted voting scheme is designed to produce the classification output. We perform experiments on 11 benchmark datasets. Experimental results clearly demonstrate the MLCE significantly outperforms six well-established multi-label classification approaches, in terms of the widely used evaluation metrics.

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Proper and accurate mix proportion is deemed to be crucial for the concrete in service to implement its structural functions in a specific environment and structure. Neither existing testing methods nor previous studies have, to date, addressed the problem of real-time and full-scale monitoring of fresh concrete mix proportion during manufacturing. Green manufacturing and safety construction are hindered by such defects. In this study, a state-of-the-art method based on improved convolutional neural network multilabel image classification is presented for mix proportion monitoring. Elaborately planned, uniformly distributed, widely covered and high-quality images of concrete mixtures were collected as dataset during experiments. Four convolutional neural networks were improved or fine-tuned based on two solutions for multilabel image classification problems, since original networks are tailored for single-label multiclassification tasks, but mix proportions are determined by multiple parameters. Various metrices for effectiveness evaluation of training and testing all indicated that four improved network models showed outstanding learning and generalization ability during training and testing. The best-performing one was embedded into executable application and equipped with hardware facilities to establish fresh concrete mix proportion monitoring system. Such system was deployed to terminals and united with mechanical and weighing sensors to establish integrated intelligent sensing system. Fresh concrete mix proportion real-time and full-scale monitoring and inaccurate mix proportion sensing and warning could be achieved simply by taking pictures and feeding pictures into such sensing system instead of conducting experiments in laboratory after specimen retention.

Learning-Based Computer-Aided Prescription Model for Parkinson's Disease: A Data-Driven Perspective

In this article, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods.

Fast Multi-Instance Multi-Label Learning

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.

Topic-Based Algorithm for Multilabel Learning With Missing Labels

In multilabel learning (MLL), each instance can be assigned by several concepts simultaneously from a class dictionary. Usually, labels in the class dictionary have semantic correlations and semantic hierarchy. Instances can be categorized into different topics. Each topic has its own label candidates, and some topics have overlapped label candidates. In this paper, we propose a novel MLL method to deal with missing labels. The proposed algorithm can recover the label matrix according to local, topic-wise, and global semantic properties. Specifically, in the global level, label consistency, label-wise semantic correlations, and semantic hierarchy are exploited; in the local level, label importance and instance-wise semantic correlations in each topic are extracted; and in the topic level, label importance similarities and instance-wise semantic similarities between topics are mined. The experimental results on five image data sets in different applications demonstrate the effectiveness of the proposed approach.

PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks

Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .

A Clinical Decision Support Framework for Heterogeneous Data Sources

To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals that it now needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilabel classification was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilabel learning by using correlations among labels (CML-kNN). The CML- kNN algorithm first exploits the dependence between every two labels to update the origin label matrix and then performs multilabel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.

Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans

Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view. Each network takes extra slices around the current slice as input to provide extra context. A sigmoid layer is used to perform multi-label classification. The output of the three networks is subsequently combined to compute a 3D bounding box for each structure. We used our approach to locate 11 structures of interest. The neural network was trained and evaluated on a large set of 1884 thorax-abdomen CT scans from patients undergoing oncological workup. Reference bounding boxes were annotated by human observers. The performance of our method was evaluated by computing the wall distance to the reference bounding boxes. The bounding boxes annotated by the first human observer were used as the reference standard for the test set. Using the best configuration, we obtained an average wall distance of [Formula: see text] mm in the test set. The second human observer achieved [Formula: see text] mm. For all structures, the results were better than those reported in previously published studies. In conclusion, we proposed an efficient method for the accurate localization of multiple organs. Our method uses multiple slices as input to provide more context around the slice under analysis, and we have shown that this improves performance. This method can easily be adapted to handle more organs.