ABLE: Attention based learning for enzyme classification

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on over sampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against eighteen other machine learning and deep learning networks, including CNN, LSTM, Bi-LSTM, GRU, and the state-of-the-art DeepEC model. We conduct experiments with two different oversampling techniques, SMOTE and ADASYN. To corroborate the obtained results, we perform extensive experimentation and statistical testing.     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

基于深度学习理论的有机合成课程教学改革

有机合成既是有机化学学习中的一个独立构成部分,也是安排于有机化学课程之中的重要学习主题。在目前的有机合成课程教学中,教师的自觉性引导不足,学生容易陷入浅层学习状态,导致教学效果不佳。结合有机化学学科特点,从有机合成课程教学现状出发,围绕深度学习教学设计的四要素——深度学习目标、挑战性学习主题、深度学习活动、持续性评价,以典型的教学片段为例阐述深度学习理论在教学实践中的应用,为相关教学工作提供参考。     

结合PBL课例的无机化学线下对分课堂教学设计与实践*

以原电池一节教学设计为例,探索了将PBL课例融入对分课堂教学模式。教学实践表明,新教学模式激发了学生的学习兴趣,提高了学生学习的积极性和主动性,提升了课程教学效果,达到了课程教学目标。对PBL课例与对分课堂有机融合的全新课堂教学模式的建设与发展进行了展望。     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

有机化学实验混合教学模式的设计与实践

通过介绍混合教学模式在有机化学实验课程教学中的设计、实施情况及效果评价与反馈等内容,与大家分享混合教学模式在有机化学实验课程教学中的应用经验与思考。     

结合PBL的思维导图法在大学化学“四大平衡”教学中的应用

以化学反应"四大平衡"的主体内容:酸碱电离平衡、配位平衡、沉淀平衡和氧化还原平衡为例,介绍结合问题导向学习的思维导图法在大学化学教学实践中的应用。