Tuberculosis

A biochemically-interpretable machine learning classifier for microbial GWAS.

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We ap...

Use of Machine Learning with Temporal Photoluminescence Signals from CdTe Quantum Dots for Temperature Measurement in Microfluidic Devices.

Because of the vital role of temperature in many biological processes studied in microfluidic devices, there is a need to develop improved temperature sensors and data analysis algorithms. The photoluminescence (PL) of nanocrystals (quantum dots) has been successfully used in microfluidic temperature devices, but the accuracy of the reconstructed temperature has been limited to about 1 K over a te...

Comparison of Xpert MTB/RIF (G4) and Xpert Ultra, including trace readouts, for the diagnosis of pulmonary tuberculosis in a TB and HIV endemic setting.

BACKGROUND: There are limited data about Xpert-Ultra performance in different settings, in HIV-infected persons, in those with a history of previous T...

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disea...

A Novel Low-Bit Quantization Strategy for Compressing Deep Neural Networks.

The increase in sophistication of neural network models in recent years has exponentially expanded memory consumption and computational cost, thereby ...

Effective metric learning with co-occurrence embedding for collaborative recommendations.

In recommender systems, matrix factorization and its variants have grown up to be dominant in collaborative filtering due to their simplicity and effe...

Memristor-Based Edge Detection for Spike Encoded Pixels.

Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and n...

Digging Deeper to Save the Old Anti-tuberculosis Target: D-Alanine-D-Alanine Ligase With a Novel Inhibitor, IMB-0283.

The emergence of drug-resistant (Mtb) has hampered treatments for tuberculosis, which consequently now require novel agents to overcome such drug res...

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted ...

Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques.

BACKGROUND: The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of ant...

Characterization of the complete chloroplast genome of (Cupressaceae), an herb to treat lumbar tuberculosis in China.

belongs to the family Cupressaceae that the branches and leaves is an important Traditional Chinese Medicine in China. In this article, the complete ...

Artificial intelligence applications for thoracic imaging.

Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural ...

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making.

Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Explo...

Chemometric challenges in development of paper-based analytical devices: Optimization and image processing.

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of comme...

Artificial Intelligence, Radiology, and Tuberculosis: A Review.

Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease...

A genetic programming-based approach to identify potential inhibitors of serine protease of .

We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of () and the ...

ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with develop active TB disease (ATB), while ot...

Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tub...

Automated Counting of Cancer Cells by Ensembling Deep Features.

High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image ...

Deep Learning Diffuse Optical Tomography.

Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast...