Identifying new lead molecules to treat cancer requires more than a decade of dedicated effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is generally validated by in vitro cellular experiments. Therefore, accurate prediction of cancer drug response is a critical and challenging task for anti-cancer drugs design and precision medicine. With the development of pharmacogenomics, the combination of efficient drug feature extraction methods and omics data has made it possible to use computational models to assist in drug response prediction. In this study, we propose DeepTTA, a novel end-to-end deep learning model that utilizes transformer for drug representation learning and a multilayer neural network for transcriptomic data prediction of the anti-cancer drug responses. Specifically, DeepTTA uses transcriptomic gene expression data and chemical substructures of drugs for drug response prediction. Compared to existing methods, DeepTTA achieved higher performance in terms of root mean square error, Pearson correlation coefficient and Spearman’s rank correlation coefficient on multiple test sets. Moreover, we discovered that anti-cancer drugs bortezomib and dactinomycin provide a potential therapeutic option with multiple clinical indications. With its excellent performance, DeepTTA is expected to be an effective method in cancer drug design.

The interaction between microribonucleic acid and long non-coding ribonucleic acid plays a very important role in biological processes, and the prediction of the one is of great significance to the study of its mechanism of action. Due to the limitations of traditional biological experiment methods, more and more computational methods are applied to this field. However, the existing methods often have problems, such as inadequate acquisition of potential features of the sequence due to simple coding and the need to manually extract features as input. We propose a deep learning model, preMLI, based on rna2vec pre-training and deep feature mining mechanism. We use rna2vec to train the ribonucleic acid (RNA) dataset and to obtain the RNA word vector representation and then mine the RNA sequence features separately and finally concatenate the two feature vectors as the input of the prediction task. The preMLI performs better than existing methods on benchmark datasets and has cross-species prediction capabilities. Experiments show that both pre-training and deep feature mining mechanisms have a positive impact on the prediction performance of the model. To be more specific, pre-training can provide more accurate word vector representations. The deep feature mining mechanism also improves the prediction performance of the model. Meanwhile, The preMLI only needs RNA sequence as the input of the model and has better cross-species prediction performance than the most advanced prediction models, which have reference value for related research.

DNA molecules, as natural information carriers, have several benefits over conventional digital storage mediums, including high information density and long-term durability. It is expected to be a promising candidate for information storage. However, despite significant research in this field, the pace of development has been slow due to the lack of complete encoding-decoding platform and simulaton-evaluation system. And the mutation in DNA sequences during synthesis and sequencing requires multiple experiments, and wet experiments can be costly. Thus, a silicon-based simulation platform is urgently needed for promoting research. Therefore, we proposed DNA Storage Designer, the first online platform to simulate the whole process of DNA storage experiments. Our platform offers classical and novel technologies and experimental settings that simulate three key processes: encoding, error simulation, and decoding for DNA storage system. First, 8 mainstream encoding methods were embedded in the encoding process to convert files to DNA sequences. Secondly, to uncover potential mutations and sequence distribution changes in actual experiments we integrate the simulation setting for five typical experiment sub-processes (synthesis, decay, PCR, sampling, and sequencing) in the error simulation stage. Finally, the corresponding decoding process realizes the conversion of DNA sequence to binary sequence. All the above simulation processes correspond to an analysis report will provide guides for better experiment design for researchers' convenience. In short, DNA Storage Designer is an easy-to-use and automatic web-server for simulating DNA storage experiments, which could advance the development of DNA storage-related research. And it is freely available for all users at: https://dmci.xmu.edu.cn/dna/ .

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