28 papers · ranked by Valyu relevance
Alejandro Moreo, Francisco Manuel, Fabrizio Sebastiani
Quantification, variously called supervised prevalence estimation or learning to quantify, is the supervised learning task of generating predictors of the relative frequencies (a.k.a. prevalence values) of the classes of interest in unlabelled data samples. While many quantification methods have been proposed in the…
Pengfei Gao, Lai Dedi, Lijiao Zhao, Yue Liang + 1 more
As a very popular multi-label classification method, Classifiers Chain has recently been widely applied to many multi-label classification tasks. However, existing Classifier Chains methods are difficult to model and exploit the underlying dependency in the label space, and often suffer from the problems of poorly…
Wenfu Liu, Jianmin Pang, Nan Li, Xin Zhou + 1 more
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…
Syed Alberuni, Sumanta Ray
Extensive evidence recognizes that proteins associated with several diseases frequently interact with each other. This leads to develop different network-based methods for uncovering the molecular workings of human diseases. These methods are based on the idea that protein interaction networks act as maps, where…
Syed Alberuni, Sumanta Ray, Hilary A. Coller
Proteins associated with multiple diseases often interact, forming disease modules that are critical for understanding disease mechanisms. This study integrates protein-protein interactions (PPIs) and Gene Ontology data using non-negative matrix factorization (NMF) to identify gene modules associated with human…
Shubo Tian, Jinfeng Zhang
The BioCreative VII Track 5 calls for participants to tackle the multi-label classification task for automated topic annotation of COVID-19 literature. In our participation, we evaluated several deep learning models built on PubMedBERT, a pre-trained language model, with different strategies addressing the challenges…
M. Priyadharshini, A. Faritha Banu, Bhisham Sharma, Subrata Chowdhury + 3 more
'Subrata Chowdhury' 'Khaled Rabie' 'Thokozani Shongwe' 'Faheem Khan'] In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from…
Elaine Cecília Gatto, Felipe Nakano Kenji, Jesse Read, Mauri Ferrandin + 2 more
Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several…
Bita Ghasemkhani, Ozlem Varliklar, Yunus Dogan, Semih Utku + 6 more
Simple Summary This study addresses the classification task in animal science, which helps organize and analyze complex data, essential for making informed decisions. It introduces Federated Multi-Label Learning (FMLL), a novel approach combining federated learning principles with a multi-label learning technique.…
Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao, Kup‐Sze Choi + 1 more
'Shitong Wang'] Abstract—Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However, the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label…
Daniel J. W. Touw, Michel van de Velden
The classifier chain is a widely used method for analyzing multi-labeled data sets. In this study, we introduce a generalization of the classifier chain: the classifier chain network. The classifier chain network enables joint estimation of model parameters, and allows to account for the influence of earlier label…
Marcel Wever
| 1. Reviewer | Prof. Dr. Eyke Hüllermeier | | --- | --- | | | Künstliche Intelligenz und Maschinelles Lernen Ludwig-Maximilians-Universität München | | 2. Reviewer | Prof. Dr. Axel-Cyrille Ngonga Ngomo | | | Data Science | | | Paderborn University | | 3. Reviewer | Prof. Dr. Bernd Bischl | | | Statistical Learning &…
Shuo Xu, Yuefu Zhang, Liang Chen, Xin An
The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic…
Abdulwahab Ali Almazroi, Nasir Ayub, Muhammad Umer
In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK…
Shunyao Wu, Zhiruo Li, Yuzhu Chen, Mingqian Zhang + 6 more
Microbiome has emerged as a promising indicator or predictor of human diseases. However, previous studies typically labeled each specimen as either healthy or with a specific disease, ignoring the prevalence of complications or comorbidities in actual cohorts, which may confound the microbial-disease associations. For…
Shanshan Li, Qingjie Liu, Xiaoling Sun, Xiangjie Kong
To address the challenge of extracting fine-grained emergency information from noisy social media during disasters, we propose HTCNN-Attn, a hierarchical multi-label deep learning model. It integrates a three-level tree-structured labeling architecture, Transformer-based global feature extraction, convolutional neural…
Mauro Andrés Nievas Offidani, Facundo Roffet, Claudio Delrieux, Maria Carolina Gonzalez Galtier + 1 more
classifiers through problem transformation, ontology engineering, and model ensembling Authors: ['Mauro Andrés Nievas Offidani' 'Facundo Roffet' 'Claudio Delrieux' 'Maria Carolina Gonzalez Galtier' 'Marcos Zárate'] Abstract. Classification is a fundamental task in machine learning. While conventional methods—such as…
Jinghou Ruan, Mingwei Wang, Deqing Liu, Maolin Chen + 2 more
'Friedhelm Schwenker'] In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads to challenges regarding feature selection. As a…
Anuj Pal, Raunak Kumar, Dhruvi Solanki, Parikshit Pareek + 2 more
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalizes this setting, but leading approaches typically rely on multimodal, structure-conditioned deep…
Abrar Rahman Abir, Md Toki Tahmid, M. Saifur Rahman
Traditional methods for mRNA subcellular localization often fail to account for multiple compartmentalization. Recent multi-label models have improved performance, but still face challenges in capturing complex localization patterns. We introduce LOCAS (Localization with Supervised Contrastive Learning), which…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…
Qinze Yu, Zhihang Dong, Xingyu Fan, Licheng Zong + 1 more
Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have been extensive studies on the statistical and computational approaches to identify (i) whether a…
Mirae Kim, Ruth Dannenfelser, Yufei Cui, Genevera Allen + 1 more
DNA methylation (DNAm) is a core gene regulatory mechanism that captures cellular responses to short- and long-term stimuli such as environmental exposures, aging, and cellular differentiation. Although DNAm has proven valuable as a baseline biomarker for aging by enabling robust characterization of disease-associated…
Samuel Genheden, Agnes Mårdh, Gustav Lahti, Ola Engkvist + 2 more
We present machine learning models for predicting the chemical context for Buchwald-Hartwig coupling reactions. Using reaction data from in-house electronic lab notebooks, we train two models: one based on single-label data and one based on multi-label data. Both models show excellent top-3 accuracy around 90%, which…
Authors not listed
Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
High throughput screening (HTS) is one of the leading techniques for hit identification in drug discovery and comprises of multiple phases, one primary and one or more confirmatory screens which result in multi-fidelity data. Noisy primary screening data are available on a large number of compounds and higher quality…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…