Search Results for author:
Found 13 papers, 5 papers with code
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data.
DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins
However, molecule generation targeted for dual protein targets still faces formidable challenges including protein 3D structure data requisition for model training, auto-regressive sampling, and model generalization for unseen targets.
MDM: Molecular Diffusion Model for 3D Molecule Generation
At the same time, the generated molecules lack enough diversity.
A Block-Based Adaptive Decoupling Framework for Graph Neural Networks
Graph neural networks (GNNs) with feature propagation have demonstrated their power in handling unstructured data.
A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection
We demonstrate the effectiveness of our algorithm on twelve large-scale datasets.
EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information
To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data.
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization
In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner.
Context awareness and embedding for biomedical event extraction
We built our models on the datasets from BioNLP Shared Task for evaluations.
Big Data Challenges in Genome Informatics
In recent years, we have witnessed a dramatic data explosion in genomics, thanks to the improvement in sequencing technologies and the drastically decreasing costs.
A Short Survey on Data Clustering Algorithms
With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research.
Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics: Evolutionary Algorithms for Bioinformatics
Since genetic algorithm was proposed by John Holland (Holland J. H., 1975) in the early 1970s, the study of evolutionary algorithm has emerged as a popular research field (Civicioglu & Besdok, 2013).
Unsupervised Learning in Genome Informatics
Here we review several unsupervised learning methods for deciphering the genome-wide patterns of those DNA regions.
Evolutionary Multimodal Optimization: A Short Survey
Unfortunately, most traditional optimization techniques focus on solving for a single optimal solution.
