•We propose two multi-label learning approaches with LIFT reduction.•The idea of fuzzy rough set attribute reduction is adopted in our approaches.•Sample selection improves the efficiency in feature ...dimension reduction.
In multi-label learning, since different labels may have some distinct characteristics of their own, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and a large amount of redundant information exists in feature space. To alleviate this problem, a multi-label learning approach FRS-LIFT is proposed, which can implement label-specific feature reduction with fuzzy rough set. Furthermore, with the idea of sample selection, another multi-label learning approach FRS-SS-LIFT is also presented, which effectively reduces the computational complexity in label-specific feature reduction. Experimental results on 10 real-world multi-label data sets show that, our methods can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi-label learning approaches.
Abstract
Vast G-quadruplexes (GQs) are primarily folded by one, two, or four G-rich oligomers, rarely with an exception. Here, we present the first NMR solution structure of a trimolecular GQ ...(tri-GQ) that is solely assembled by the self-trimerization of d(GTTAGG), preferentially in Na+ solution tolerant to an equal amount of K+ cation. Eight guanines from three asymmetrically folded strands of d(GTTAGG) are organized into a two-tetrad core, which features a broken G-column and two width-irregular grooves. Fast strand exchanges on a timescale of second at 17°C spontaneously occur between folded tri-GQ and unfolded single-strand of d(GTTAGG) that both species coexist in dynamic equilibrium. Thus, this tri-GQ is not just simply a static assembly but rather a dynamic assembly. Moreover, another minor tetra-GQ that has putatively tetrameric (2+2) antiparallel topology becomes noticeable only at an extremely high strand concentration above 18 mM. The major tri-GQ and minor tetra-GQ are considered to be mutually related, and their reversible interconversion pathways are proposed accordingly. The sequence d(GTTAGG) could be regarded as either a reading frame shifted single repeat of human telomeric DNA or a 1.5 repeat of Bombyx mori telomeric DNA. Overall, our findings provide new insight into GQs and expect more functional applications.
Triple-G: a new MGRS and attribute reduction Ba, Jing; Liu, Keyu; Ju, Hengrong ...
International journal of machine learning and cybernetics,
02/2022, Letnik:
13, Številka:
2
Journal Article
Recenzirano
Different from classical rough set, Multigranulation Rough Set (MGRS) is frequently designed for approximating target through using multiple results of information granulation. Presently, though many ...forms of MGRS have been intensively explored, most of them are constructed based on the homogeneous information granulation with respect to different scales or levels. They lack the multi-view which involves the results of heterogeneous information granulation. To fill such a gap, a Triple-
G
MGRS is developed. Such a Triple-
G
is composed of three different heterogeneous information granulations: (1) nei
G
hborhood based information granulation; (2)
G
ap based information granulation; (3)
G
ranular ball based information granulation. Neighborhood provides a parameterized mechanism while gap and granular ball offer two representative data-adaptive strategies for performing information granulation. Immediately, both optimistic and pessimistic MGRS can be re-constructed. Furthermore, the problem of attribute reduction is also addressed based on the proposed models. Not only the forward greedy searching is used for deriving the Triple-
G
MGRS related reducts, but also an attribute grouping based accelerator is reported for further speeding up the process of searching reducts. The experimental results over 20 UCI data sets demonstrate the follows: (1) from the viewpoint of the generalization performance, the reducts obtained by our Triple-
G
MGRS is superior to those obtained by previous researches; (2) attribute grouping does speed up the process of searching reducts.
Background
Accumulating evidence announces that aberrantly expressed circRNAs were closely related to the development of human cancers. However, the role and mechanism of multiple circRNAs remain ...unclear. Our work aimed to disclose the functional role and mechanism of circ_0081054 in melanoma.
Methods
Quantitative real‐time polymerase chain reaction assay was utilized to detect circ_0081054, microRNA‐637 (miR‐637) and RAB9A (member RAS oncogene family) mRNA expression. Cell proliferative ability was evaluated via Cell Counting Kit‐8 and colony formation assay. Cell invasion was assessed by using wound healing assay.
Results
The significant upregulation of circ_0081054 was detected in melanoma tissues and cells. The proliferation, migration, glycolytic metabolism, and angiogenesis in melanoma cells were suppressed, while apoptosis was promoted following the silence of circ_0081054. In addition, circ_0081054 could target miR‐637, and miR‐637 inhibitor could reverse the effects of circ_0081054 deficiency. Furthermore, RAB9A was a target gene for miR‐637 and RAB9A overexpression could reverse the effects of miR‐637 overexpression. In addition, the deficiency of circ_0081054 hampered tumor growth in vivo. Moreover, circ_0081054 could regulate RAB9A expression by sponging miR‐637.
Conclusion
All results indicated that circ_0081054 promoted the malignant behaviors of melanoma cells partly by regulating the miR‐637/RAB9A molecular axis.
Attribute reduction is a paradigm covering both theories and techniques of selecting required attributes with constraints related to rough set. Currently, though various searching strategies have ...been developed for achieving such a purpose, few of them take the rewards of identifying attributes into account. In this study, inspired by the popular reinforcement learning mechanism, a
Q
-learning based procedure is designed to search qualified attributes and then construct the expected reduct. Specifically, state is regarded as the temporary result of selected attributes, action is regarded as variation of such a temporary result if random strategy is performed. Immediately, the reward can be obtained, which offers guidance on identifying attributes with the greatest reward. Moreover, considering the random factors emerge in our scheme, an ensemble device is also used to further improve classification performance of selected attributes in reduct. Finally, comprehensive experiments over a total of 15 UCI datasets clearly validates the superiorities of our study against 5 state-of-the-art approaches.
•A placebo-controlled trial was conducted to investigate the efficacy of acupoint application for OAB patients.•Statistically significant decreases in OABSS scores, OAB-q scores, and TCM syndrome ...scores were observed after the intervention.•NGF and NGF/Cr factors were observed meaningful changes in the treatment group.•The mechanism of “Dinggui” herbs for treating OAB symptoms was discussed.
Conventional treatments for alleviating the symptoms of Overactive bladder (OAB) have been reported to have limited efficacy and a high rate of side effects. Traditional Chinese medicine (TCM) has been used in Asia countries because of its low side effects and being easy to operate. To confirm the efficacy of acupoint application treatment for alleviating OAB symptoms, a randomized and placebo-controlled pilot trial was conducted in this study.
All participants were randomly allocated into a treatment group or control group, receiving either a “Dinggui” acupoint application or placebo treatment for 4 weeks. The outcome measures were OAB symptom scores (OABSS), OAB questionnaire (OAB-q) scores, and TCM syndrome scores. Urine nerve growth factor (NGF) level, NGF normalized to urine creatinine (NGF/Cr), and maximum flow rate (Qmax) were also measured to evaluate the OAB symptoms.
In total, 69 participants were included with 34 in the treatment group and 35 in the placebo-treated group. Treatment with “Dinggui” acupoint application showed a statistically significant decrease in OABSS scores (8.10±1.54 to 3.67±1.77), OAB-q scores (61.43±13.93 to 38.13±15.42), and TCM syndrome scores (15.60±5.98 to 9.20±4.82). The NGF and NGF/Cr were also observed meaningful changes in a decrease from 379.68 to 136.17 pg/ml and from 0.30 to 0.16 pg/mg, respectively. The Qmax value showed a significant increase from 14.40 to 24.05 ml/s.
Treatment with “Dinggui” acupoint application could be considered an effective and alternative therapy for OAB management. Further studies with larger sample sizes and longer treatment periods are needed to investigate.
Generator leading phase operation can not only absorb excess reactive power under small load operation mode, but also smoothly regulate grid voltage to cope with abrupt changes in grid operation. ...Based on the summer small load typical operation mode of Gansu Power Grid, four calculation conditions are selected to calculate power flow, and the influence of generator leading phase operation on grid voltage and loss is simulated and analyzed. The simulation results show that no matter how the active power output distribution of generators changes in the power grid, the voltage level of the power grid under small load can be significantly improved by generator leading phase operation. However, the grid loss will increase when the generator is in leading phase operation.
Neighborhood collaborative classifiers Suping Xu; Xibei Yang; Tsang, Eric C. C. ...
2016 International Conference on Machine Learning and Cybernetics (ICMLC),
2016-July, Letnik:
1
Conference Proceeding
In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we ...propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.
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