XiaoMi-AI文件搜索系统
World File Search Systemplays
改善脊髓损伤后的行走能力
步行是日常生活的基本活动之一，它让我们能够四处走动并与周围环境建立联系。除了从 A 点到 B 点的交通之外，它在许多日常任务中发挥着重要作用，包括家庭、社交和休闲活动。因此，步行在确保独立性、促进社交互动、提高整体生活质量方面发挥着关键作用。此外，它是维持身体活动的基石，从而保持整体健康。7,8
ZNF238 抗体（C 端）
功能转录阻遏物，在各种发育过程（如肌生成和脑发育）中发挥作用。通过直接抑制骨骼肌生成的 2 种抑制剂 ID2 和 ID3 的表达，在肌生成中发挥关键作用。还参与控制祖细胞的细胞分裂和调节有丝分裂后皮质神经元的存活。特异性结合包含 E 盒核心的共识 DNA 序列 5'-[AC]ACATCTG[GT][AC]-3'，并通过募集染色质重塑多蛋白复合物发挥作用。也可能在细胞核染色体的组织中发挥作用。
SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction
Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.