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Found 12 papers, 6 papers with code
Towards a Rigorous Evaluation of Time-series Anomaly Detection
Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden.
TargetNet: Functional microRNA Target Prediction with Deep Neural Networks
Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction.
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks.
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph.
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.
Ranked #17 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
The experimental results with real-world data confirm the effectiveness of the system and models.
DNA Steganalysis Using Deep Recurrent Neural Networks
We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework.
Neural Universal Discrete Denoiser
We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser.
deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks
MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them.
Deep Learning in Bioinformatics
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics.
DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks
A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary between a pair of exon and intron.
NASCUP: Nucleic Acid Sequence Classification by Universal Probability
Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory.
Genomics Information Theory Information Theory
