Search Results for author:
Found 18 papers, 8 papers with code
Towards Enabling FAIR Dataspaces Using Large Language Models
Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture.
Facilitating the sharing of electrophysiology data analysis results through in-depth provenance capture
We present Alpaca (Automated Lightweight Provenance Capture), a tool that captures fine-grained provenance information with minimal user intervention when running data analysis pipelines implemented in Python scripts.
From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer
Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios.
Überprüfung von Integritätsbedingungen in Deduktiven Datenbanken
Advancements in computer science and AI lead to the development of larger, more complex knowledge bases.
A Biomedical Knowledge Graph for Biomarker Discovery in Cancer
However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies.
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Explainable artificial intelligence (XAI) aims to overcome the opaqueness of black-box models and provide transparency in how AI systems make decisions.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Question Answering Over Biological Knowledge Graph via Amazon Alexa
However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies.
Interpreting Black-box Machine Learning Models for High Dimensional Datasets
Many real-life datasets, however, are of increasingly high dimensionality, where a large number of features may be irrelevant for both supervised and unsupervised learning tasks.
Towards General Deep Leakage in Federated Learning
We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.
Secure Evaluation of Knowledge Graph Merging Gain
During the protocol, the intersection between the two knowledge graphs is determined in a privacy preserving fashion.
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language
The exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behaviour like online harassment, cyberbullying, and hate speech.
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world.
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data
In this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data.
Structured Query Construction via Knowledge Graph Embedding
At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query.
Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking.
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions.
Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing
The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs.
Knowledge Representation on the Web revisited: Tools for Prototype Based Ontologies
In this paper we present a practical implementation of a different kind of knowledge representation based on Prototypes.
