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Defining meaningful feature (molecule) combinations can enhance the study of disease diagnosis and prognosis. However, feature combinations are complex and various in biosystems, and the existing methods examine the feature cooperation in a single, fixed pattern for all feature pairs, such as linear combination. To identify the appropriate combination between two features and evaluate feature combination more comprehensively, this paper adopts kernel functions to study feature relationships and proposes a new omics data analysis method KF- k -TSP. Besides linear combination, KF- k -TSP also explores the nonlinear combination of features, and allows hybridizing multiple kernel functions to evaluate feature interaction from multiple views. KF- k -TSP selects k > 0 top-scoring pairs to build an ensemble classifier. Experimental results show that KF- k -TSP with multiple kernel functions which evaluates feature combinations from multiple views is better than that with only one kernel function. Meanwhile, KF- k -TSP performs better than TSP family algorithms and the previous methods based on conversion strategy in most cases. It performs similarly to the popular machine learning methods in omics data analysis, but involves fewer feature pairs. In the procedure of physiological and pathological changes, molecular interactions can be both linear and nonlinear. Hence, KF- k -TSP, which can measure molecular combination from multiple perspectives, can help to mine information closely related to physiological and pathological changes and study disease mechanism. [ABSTRACT FROM AUTHOR]

Identifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a strong discriminating ability tends to have many edges between the same group samples and few edges between the different group samples. At the same time, FS-SN removes redundant features according to the gravitational interaction between features. To show the validation of FS-SN, it was compared on ten public datasets with ERGS, mRMR, ReliefF, ATSD-DN, and INDEED which are efficient in omics data analysis. Experimental results show that FS-SN performed better than the compared methods in accuracy, sensitivity and specificity in most cases. Hence, FS-SN making use of the topology of the sample network is effective for analyzing omics data, it can identify key features that reflect the occurrence and development of diseases, and reveal the underlying biological mechanism. [ABSTRACT FROM AUTHOR]

We report a simple and green method for fabricating SnS2 modified moss composites for removal of heavy metal (Cr(VI)) considering the fact that the issue of wastewater caused by the discharge of heavy metal is becoming increasingly serious. More importantly, this kind of functional materials that are capable of removing effectively Cr(VI) from wastewater for water purification is quite few on the market. Hence, in the paper, such SnS2 decorated moss photocatalysts for removal of heavy metal (Cr(VI)) were designed and prepared via a simple and green method: chemical bath reaction. The as-designed materials (C@SnS integrate the independent properties of moss (with excellent performance in absorption of heavy metal) and SnS2 (with good photocatalytic reduction of Cr(VI) under visible light irradiation), showing a faster photocatalytic rate than those independent components. The experiment results indicate that more than 98.5% of Cr(VI) was reduced under visible light irradiation for 70min, while only 18.2% and 84.4% of Cr(VI) were removed in the presence of C-moss and pure SnS2 at the same condition, respectively. Furthermore, the as-designed C@SnS2 has good recyclability in the removal of Cr(VI). We believe such simple and green method to prepare photocatalyst for wastewater treatment with excellent performance will provide a good platform to design new photocatalytic materials for water purification. SnS2-decorated moss photocatalysts for the removal of heavy metal (Cr(VI)) were designed and prepared via a simple and green method: chemical bath reaction. The experimental results indicate that more than 98.5% of Cr(VI) were reduced under visible light irradiation for 70 mins. Furthermore, the as-designed C@SnS2 has good recyclability in the removal of Cr(VI). [ABSTRACT FROM AUTHOR]