Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms.

Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform.

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks.

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs).

Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning.

Accurate automated medical image recognition, including classification and segmentation, is one of the most challenging tasks in medical image analysis. Recently, deep learning methods have achieved remarkable success in medical image classification and segmentation, clearly becoming the state-of-the-art methods. However, most of these methods are unable to provide uncertainty quantification (UQ) for their output, often being overconfident, which can lead to disastrous consequences.

Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin.

Background: The SARS-CoV-2 pandemic is one of  the greatest  global medical and social challenges that have emerged in recent history. Human coronavirus strains discovered during previous SARS outbreaks have been hypothesized to pass from bats to humans using intermediate hosts, e.g.

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening.

Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias.

Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies.

Spatial bias continues to be a major challenge in high-throughput screening technologies. Its successful detection and elimination are critical for identifying the most promising drug candidates. Here, we examine experimental small molecule assays from the popular ChemBank database and show that screening data are widely affected by both assay-specific and plate-specific spatial biases.

Detecting and removing multiplicative spatial bias in high-throughput screening technologies.

Motivation: Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only.