QOMIC: quantum optimization for motif identification.

Motivation: Network motif identification (MI) problem aims to find topological patterns in biological networks. Identifying disjoint motifs is a computationally challenging problem using classical computers. Quantum computers enable solving high complexity problems which do not scale using classical computers.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput.

Utilisation of Waste Sludge from Drinking Water Treatment as a Filler Material in Hot Mix Asphalt.

This research investigated the suitability of using sludge from the treatment of drinking water in hot mix asphalt (HMA) as a filler material. The storage and environmental impact of sludge is an enormous problem, especially for countries with large populations. Two different types of sludges, ferric chloride (FC) and aluminium sulphate (AS), were used as a filler material in HMA.

Morphological Profiling for Drug Discovery in the Era of Deep Learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput.

QuTIE: quantum optimization for target identification by enzymes.

Summary: Target identification by enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is a NP-hard problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this article, we develop the first quantum optimization solution, called (quantum optimization for target identification by enzymes), to this NP-hard problem.