Sensors based on electronic textiles (e‐textiles) have become increasingly prominent in the field of biomechanical monitoring technology due to multiple properties such as being lightweight, flexible, and comfortable, with increasing potential in incorporating into long‐term monitoring devices. Previous research has been conducted into textile strain sensors based on graphene for human motion monitoring, however most graphene e‐textile strain sensors exhibit poor sensitivity and stretchability. To our knowledge, no previous research has looked at knitted graphene‐based fabrics in regards to the fabric composition of the substrate. In this paper, we propose a graphene/fabric composite sensor using a cost‐effective dip coating method of an acrylic/Spandex knit fabric, and further explores its mechanical, electrical, and sensing properties. The developed graphene/textile composite sensor has a wide sensing range (up to 344%) and exhibits a good sensitivity with a high gauge factor of up to 16. As a wearable sensor, our sensing fabric can detect both large and subtle human motions and is able to distinguish between various ranges of joint movements, demonstrating its ability to function as a human motion monitoring system. Our sensor further exhibits the ability to be used as a supercapacitor or capacitive pressure sensor.

The identification of long noncoding RNA (lncRNA)-disease associations is of great value for disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA-disease associations with computational methods. However, the existing methods do not sufficiently extract key features during data processing, and the learning model parts are either less powerful or overly complex. Therefore, there is still potential to achieve better predictive performance by improving these two aspects. In this work, we propose a novel lncRNA-disease association prediction method LDAformer based on topological feature extraction and Transformer encoder. We construct the heterogeneous network by integrating the associations between lncRNAs, diseases and micro RNAs (miRNAs). Intra-class similarities and inter-class associations are presented as the lncRNA-disease-miRNA weighted adjacency matrix to unify semantics. Next, we design a topological feature extraction process to further obtain multi-hop topological pathway features latent in the adjacency matrix. Finally, to capture the interdependencies between heterogeneous pathways, a Transformer encoder based on the global self-attention mechanism is employed to predict lncRNA-disease associations. The efficient feature extraction and the intuitive and powerful learning model lead to ideal performance. The results of computational experiments on two datasets show that our method outperforms the state-of-the-art baseline methods. Additionally, case studies further indicate its capability to discover new associations accurately.

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