COVID-19

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear.

Machine learning prediction of malaria vaccine efficacy based on antibody profiles.

Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled human malaria infection (CHMI). Humoral and cellular immunity contribute to vaccine efficacy but only limited information about the implicated Pf-specific...

Cancer diagnosis in the post-coronavirus disease era: the promising role of telepathology and artificial intelligence.

Two-part predictive modeling for COVID-19 cases and deaths in the U.S.

COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive...

TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.

Genome-wide association studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, whi...

Functional Neural Networks for High-Dimensional Genetic Data Analysis.

Artificial intelligence (AI) is a thriving research field with many successful applications in areas such as computer vision and speech recognition. M...

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.

IMTRODUCTION: The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Sev...

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation.

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-a...

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EE...

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription facto...

Reinforcement learning-guided control strategies for CAR T-cell activation and expansion.

Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of ...

Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation.

Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, ...

Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology.

Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growt...

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring appli...

Machine learning tool as an enabler for rapid quantification of monoclonal antibodies N-glycans using fluorescence detector.

Liquid chromatography-mass spectrometry (LC-MS) is widely used for identification and quantification of N-glycans of monoclonal antibodies (mAbs), owi...

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset.

Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this op...

Detection and recognition of the invasive species, Hylurgus ligniperda, in traps, based on a cascaded convolution neural network.

BACKGROUND: Hylurgus ligniperda, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it ...

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million pe...

Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model.

Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red gin...

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and diseas...