73 research outputs found
DNA Steganalysis Using Deep Recurrent Neural Networks
Recent advances in next-generation sequencing technologies have facilitated
the use of deoxyribonucleic acid (DNA) as a novel covert channels in
steganography. There are various methods that exist in other domains to detect
hidden messages in conventional covert channels. However, they have not been
applied to DNA steganography. The current most common detection approaches,
namely frequency analysis-based methods, often overlook important signals when
directly applied to DNA steganography because those methods depend on the
distribution of the number of sequence characters. To address this limitation,
we propose a general sequence learning-based DNA steganalysis framework. The
proposed approach learns the intrinsic distribution of coding and non-coding
sequences and detects hidden messages by exploiting distribution variations
after hiding these messages. Using deep recurrent neural networks (RNNs), our
framework identifies the distribution variations by using the classification
score to predict whether a sequence is to be a coding or non-coding sequence.
We compare our proposed method to various existing methods and biological
sequence analysis methods implemented on top of our framework. According to our
experimental results, our approach delivers a robust detection performance
compared to other tools
Comprehensive ensemble in QSAR prediction for drug discovery
Background
Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject.
Results
The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at
http://data.snu.ac.kr/QSAR/
Conclusions
We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.Publication costs were funded by Seoul National University. This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) [2014M3C9A3063541, 2018R1A2B3001628], the Brain Korea 21 Plus Project in 2018, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea [HI15C3224]. The funding bodies did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript
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