Search Results for author:
Found 7 papers, 2 papers with code
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Our models ingest a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value.
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder.
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data
We explore and compare three methods in order to perform unsupervised stress detection: 1) traditional K-Means clustering with engineered time and frequency domain features 2) convolutional autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on the raw RRI measurements combined with DBSCAN clustering and K-Nearest-Neighbors classification.
Named Entity Recognition in the Legal Domain using a Pointer Generator Network
Named Entity Recognition (NER) is the task of identifying and classifying named entities in unstructured text.
Legal Entity Extraction using a Pointer Generator Network
To overcome the challenges of our noisy training data, e. g., text extraction errors and/or typos and unknown label indices, we frame the NER task as a sequence generation task (seq2seq) and train a pointer generator network to generate the entities in the document rather than label them.
