COVID-19

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associat...

AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples.

The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specif...

Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron.

Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psy...

Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts.

Ambient ozone (O) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological condi...

An innovative ensemble model based on deep learning for predicting COVID-19 infection.

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare...

Applications of artificial intelligence in clinical laboratory genomics.

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human ...

AI-ChatGPT/GPT-4: An Booster for the Development of Physical Medicine and Rehabilitation in the New Era!

Artificial intelligence (AI) has been driving the continuous development of the Physical Medicine and Rehabilitation (PM&R) fields. The latest release...

DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants.

Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic vari...

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.

The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that ...

Probing the origins of programmed death ligand-1 inhibition by implementing machine learning-assisted sequential virtual screening techniques.

PD-L1 is a key immunotarget involved in binding to its receptor PD-1. PD-L1/PD-1 interface blocking using antibodies (or small molecules) is the centr...

Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition.

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μs level temporal resolution, has prominent advantages in many spec...

MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy.

UNLABELLED: Hepatic hemangioma (HH) and hepatoblastoma (HBL) are common pediatric liver tumors and present with similar clinical manifestations with l...

A Review of the Systemic Manifestations of Hepatitis B Virus Infection, Hepatitis D Virus, Hepatocellular Carcinoma, and Emerging Therapies.

Chronic hepatitis B virus (HBV) infection affects about 262 million people worldwide, leading to over 820,000 deaths each year primarily due to cirrho...

Exploring the potential of FDA approved anti-diabetic drugs for repurposing against COVID-19: a core combination of multiple computational strategies and integrated artificial intelligence.

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of conce...

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies.

Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mecha...

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19.

PURPOSE: As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatm...

Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.

Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for m...

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breas...

Unsupervised domain adaptation for Covid-19 classification based on balanced slice Wasserstein distance.

Covid-19 has swept the world since 2020, taking millions of lives. In order to seek a rapid diagnosis of Covid-19, deep learning-based Covid-19 classi...

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed ...