Advances in omics technologies allow for holistic studies into biological systems. These
studies rely on integrative data analysis techniques to obtain a comprehensive view of the …

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative
imprint across multiple disciplines. Within computational biology, it is expediting progress in …

Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

Often real-world datasets are incomplete and contain some missing attribute values.
Furthermore, many data mining and machine learning techniques cannot directly handle …

Humanity's quest for safe, resilient, and liveable cities has prompted research into the
application of computational tools in the design and development of sustainable smart cities …

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of
data generated by single-cell technologies are high dimensional, sparse, and …

Integrating diverse types of biological data is essential for a holistic understanding of cancer
biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity …

Abstract ydata-profiling is an open-source Python package for advanced exploratory data
analysis that enables users to generate data profiling reports in a simple, fast, and efficient …

The digital transformation of modern cities by integrating advanced information,
communication, and computing technologies has marked the epoch of data-driven smart city …

Data are a strategic resource for industrial production, and an efficient data-mining process
will increase productivity. However, there exist many missing values in data collected in real …