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Article
Semi-supervised feature selection based on discernibility matrix and mutual information
Feature selection is a vital technique for reducing data dimensionality. While many granular computing-based feature selection algorithms have been proposed, most have been regarded as a supervised learning ta...
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Article
Neighborhood relation-based incremental label propagation algorithm for partially labeled hybrid data
Label propagation can rapidly predict the labels of unlabeled objects as the correct answers from a small amount of given label information, which can enhance the performance of subsequent machine learning tas...
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Article
Multi-label feature selection via spectral clustering-based label enhancement and manifold distribution consistency
Multi-label feature selection can effectively improve the performance and efficiency of subsequent learning tasks by selecting important features within multi-label data. However, for handling multiple labels,...
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Article
Coarse-to-fine cascaded 3D hand reconstruction based on SSGC and MHSA
Recently, graph convolution networks have become the mainstream methods in 3D hand pose and mesh estimation, but there are still some issues hindering its further development. First, the way that previous rese...
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Article
Incremental feature selection based on uncertainty measure for dynamic interval-valued data
Feature selection is an important strategy for knowledge reduction in rough set. Interval-valued data, as an extension of single values, can better express uncertain information from the perspective of uncerta...
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Article
Open AccessVisualisation of \(C\!P\) -violation effects in decay-time-dependent analyses of multibody B-meson decays
Decay-time-dependent \(C\!P\) C P ...
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Article
Multiple reference points-based multi-objective feature selection for multi-label learning
In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has become a h...
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Article
Open AccessVenetoclax-based therapy for relapsed or refractory acute myeloid leukemia: latest updates from the 2023 ASH annual meeting
Patients with relapsed or refractory (R/R) acute myeloid leukemia (AML) often exhibit limited responses to traditional chemotherapy, resulting in poor prognosis. The combination of venet...
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Article
Tumor-derived exosomes induce initial activation by exosomal CD19 antigen but impair the function of CD19-specific CAR T-cells via TGF-β signaling
Tumor-derived exosomes (TEXs) enriched in immune suppressive molecules predominantly drive T-cell dysfunction and impair antitumor immunity. Chimeric antigen receptor (CAR) T-cell therapy has emerged as a prom...
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Article
Open AccessLong-term outcomes with HLX01 (HanliKang®), a rituximab biosimilar, in previously untreated patients with diffuse large B-cell lymphoma: 5-year follow-up results of the phase 3 HLX01-NHL03 study
HLX01 (HanliKang®) is a rituximab biosimilar that showed bioequivalence to reference rituximab in untreated CD20-positive diffuse large B-cell lymphoma (DLBCL) in the phase 3 HLX01-NHL03 study. Here, we report th...
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Article
Open AccessSafety and feasibility of anti-CD19 CAR T cells expressing inducible IL-7 and CCL19 in patients with relapsed or refractory large B-cell lymphoma
Although CD19-specific chimeric antigen receptor (CAR) T cells are curative for patients with relapsed or refractory large B-cell lymphoma (R/R LBCL), disease relapse with tumor antigen-positive remains a chal...
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Article
Analysis of the genomic landscape of primary central nervous system lymphoma using whole-genome sequencing in Chinese patients
Primary central nervous system lymphoma (PCNSL) is an uncommon non-Hodgkin’s lymphoma with poor prognosis. This study aimed to depict the genetic landscape of Chinese PCNSLs. Whole-genome sequencing was perfor...
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Article
Granular ball-based label enhancement for dimensionality reduction in multi-label data
As an important preprocessing procedure, dimensionality reduction for multi-label learning is an effective way to solve the challenge caused by high-dimensionality data. Most existing dimensionality reduction ...
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Article
Neighbourhood discernibility degree-based semisupervised feature selection for partially labelled mixed-type data with granular ball
Feature selection can effectively decrease data dimensions by selecting a relevant feature subset. Rough set theory provides a powerful theoretical framework for the feature selection of categorical data with ...
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Article
Open AccessVenetoclax-based therapy for relapsed or refractory acute myeloid leukemia: latest updates from the 2022 ASH annual meeting
Venetoclax (VEN), the first selective Bcl-2 inhibitor, has shown efficacy and safety both as monotherapy and in combination with other agents in the treatment of newly diagnosed acute myeloid leukemia (AML), w...
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Article
Heavy flavour physics and CP violation at LHCb: A ten-year review
Heavy flavour physics provides excellent opportunities to indirectly search for new physics at very high energy scales and to study hadron properties for deep understanding of the strong interaction. The LHCb ...
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Article
Information gain-based semi-supervised feature selection for hybrid data
Information gain, as an important feature measure, plays a vital role in the process of feature selection. Most of existing information gain-based feature selection algorithms are developed on data with single...
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Article
Open AccessRituximab with high-dose methotrexate is effective and cost-effective in newly diagnosed primary central nervous system lymphoma
Induction chemotherapy based on high-dose methotrexate is considered as the standard approach for newly diagnosed primary central nervous system lymphomas (PCNSLs). However, the best combination chemotherapeut...
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Article
Local rough set-based feature selection for label distribution learning with incomplete labels
Label distribution learning, as a new learning paradigm under the machine learning framework, is widely applied to address label ambiguity. However, most existing label distribution learning methods require co...
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Article
Open AccessA novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT
This study aimed to develop a novel analytic approach based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic signature (RS) and International Prognost...
