HIV/AIDS

Quantitative salivary gland SPECT/CT using deep convolutional neural networks.

Quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland function. However, gland segmentation and quantitation of gland uptake is challenging. We develop a salivary gland SPECT/CT with automated segmentation using a deep convolutional neural network (CNN). The protocol comprises SPECT/CT at 20 min, sialagog...

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning.

A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided i...

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides.

Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alo...

Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.

Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations...

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.

High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These techno...

Pneumocystis pneumonia: An important consideration when investigating artificial intelligence-based methods in the radiological diagnosis of COVID-19.

Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype.

Artificial intelligence (AI) has made increasing inroads in clinical medicine. In surgery, machine learning-based algorithms are being studied for use...

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls.

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritabili...

Exploring the spatial reasoning ability of neural models in human IQ tests.

Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has...

Robot-Based Assessment of HIV-Related Motor and Cognitive Impairment for Neurorehabilitation.

There is a pressing need for strategies to slow or treat the progression of functional decline in people living with HIV. This paper explores a novel ...

Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response.

Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping o...

Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning.

The presence of pathogen-specific antibodies in an individual's blood-sample is used as an indication of previous exposure and infection to that speci...

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the ...

Comparison of an Artificial Intelligence-Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis: A Randomized Clinical Trial.

IMPORTANCE: Decision aids can help inform appropriate selection of total knee replacement (TKR) for advanced knee osteoarthritis (OA). However, few de...

Exploration of text matching methods in Chinese disease Q&A systems: A method using ensemble based on BERT and boosted tree models.

BACKGROUND: Text matching is one of the basic tasks in the field of natural language processing. Owing to the particularity of Chinese language and me...

Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.

Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a predictio...

Classification of intestinal T-cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status.

In coeliac disease (CeD), immune-mediated small intestinal damage is precipitated by gluten, leading to variable symptoms and complications, occasiona...

Evolution of drug resistance in HIV protease.

BACKGROUND: Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resi...

Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team.

The use of artificial intelligence (AI) algorithms in the field of radiology is becoming more common. Several studies have demonstrated the potential ...

Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a shor...