Search Results for author: Ali Oskooei

Found 7 papers, 2 papers with code

Legal Entity Extraction using a Pointer Generator Network

no code implementations International Conference on Data Mining Workshops (ICDMW) 2022 Stavroula Skylaki, Ali Oskooei, Omar Bari, Nadja Herger, Zac Kriegman

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.

named-entity-recognition Named Entity Recognition +2

DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data

no code implementations18 Nov 2019 Ali Oskooei, Sophie Mai Chau, Jonas Weiss, Arvind Sridhar, María Rodríguez Martínez, Bruno Michel

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.

Clustering Heart Rate Variability +2

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning

no code implementations29 Aug 2019 Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Karsten Borgwardt, María Rodríguez Martínez

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).

reinforcement-learning Reinforcement Learning (RL)

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

1 code implementation25 Apr 2019 Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

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.

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

1 code implementation16 Nov 2018 Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

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.

Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer

no code implementations18 Aug 2018 Ali Oskooei, Matteo Manica, Roland Mathis, Maria Rodriguez Martinez

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.

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