10 papers · ranked by Valyu relevance
Qihong Song, Haize Hu, Tebo Dai
Code search aims to search for code snippets from large codebase that are semantically related to natural query statements. Deep learning is a valuable method for solving code search tasks in which the quality of training data directly impacts the performance of deep-learning models. However, most existing…
Valeriy Berezovskiy, Anastasia Gorodilova, Ekaterina Trofimova, Andrey Ustyuzhanin + 1 more
'Andrey Ustyuzhanin' 'Syed Hassan Shah'] Program code has recently become a valuable active data source for training various data science models, from code classification to controlled code synthesis. Annotating code snippets play an essential role in such tasks. This article presents a novel approach that leverages…
Fang-Yi Su, Gia-Han Ngo, Ben Phan, Jung-Hsien Chiang
Biomedical relation extraction often involves datasets with implicit constraints, where structural, syntactic, or semantic rules must be strictly preserved to maintain data integrity. Traditional data augmentation techniques struggle in these scenarios, as they risk violating domain-specific constraints. To address…
Brian Kenji Iwana, Seiichi Uchida, Friedhelm Schwenker
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem…
Paweł Błażej, Małgorzata Wnetrzak, Dorota Mackiewicz, Paweł Mackiewicz
Compounds including non-canonical amino acids or other artificially designed molecules can find a lot of applications in medicine, industry and biotechnology. They can be produced thanks to the modification or extension of the standard genetic code (SGC). Such peptides or proteins including the non-canonical amino…
Hyunjung Lee, Utku Ozbulak, Homin Park, Stephen Depuydt + 2 more
'Wesley De Neve' 'Joris Vankerschaver'] Background Deep neural networks (DNNs) have the potential to revolutionize our understanding and treatment of genetic diseases. An inherent limitation of deep neural networks, however, is their high demand for data during training. To overcome this challenge, other fields, such…
Yiyang Yu, Shivani Muthukumar, Peter K Koo
Deep neural networks (DNNs) have been widely applied to predict the molecular functions of regulatory regions in the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typically generate limited amounts of data, constrained by the…
Nikita Janakarajan, Mara Graziani, Maria Rodriguez Martinez
Working with transcriptomic data is challenging in deep learning applications due to its high dimensionality and low patient numbers. Deep learning models tend to overfit this data and do not generalize well on out-of-distribution samples and new cohorts. Data augmentation strategies help alleviate this problem by…
Andrew G Duncan, Jennifer A Mitchell, Alan M Moses
Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the complexity of the sequence to regulatory function mapping (the cis-regulatory code), it…
Pawel M. Mordaka, Kitty Clouston, Jing Cui, Andre Holzer + 3 more
Genome scale engineering has enabled codon compression of the universal genetic code of up to three codons in E. coli, providing the means for genetic code expansion. To go much beyond this number, smaller and simpler genetic systems are needed to avoid significant technical challenges. Chloroplast genomes offer…