The paper categorizes and reviews the state-of-the-art approaches to the partially
supervised learning (PSL) task. Special emphasis is put on the fields of pattern recognition …

Advancements in genomic research such as high-throughput sequencing techniques have
driven modern genomic studies into" big data" disciplines. This data explosion is constantly …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Drug-induced liver injury (DILI) has been the single most frequent cause of safety-related
drug marketing withdrawals for the past 50 years. Recently, deep learning (DL) has been …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

The identification of human-virus protein-protein interactions (PPIs) is an essential and
challenging research topic, potentially providing a mechanistic understanding of viral …

Abstract Background Named Entity Recognition (NER) is a key task in biomedical text
mining. Accurate NER systems require task-specific, manually-annotated datasets, which …

As nontechnical losses in power systems have recently become a global concern, electricity
fraud detection models attracted increasing academic interest. The wide application of smart …

While leading to millions of people's deaths every year the treatment of viral infectious
diseases remains a huge public health challenge. Therefore, an in-depth understanding of …

Morphological attributes from histopathological images and molecular profiles from genomic
data are important information to drive diagnosis, prognosis, and therapy of cancers. By …