COVID-19

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to ...

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system.

Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.

OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical ...

Mediated Synthesis of Silver Nanoparticles and Its Evaluation of Antineoplastic Activity to Combat Colorectal Cancer Cell Line.

Several formulations of herbal plants have been extensively applied to treat diseases. ( is an Iranian traditional plant with a wide range of benefit...

Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to id...

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long be...

Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.

Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting t...

Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery.

Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for th...

Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting pr...

Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model.

Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in ol...

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype.

MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing vari...

Automatic PAP Mask Sizing with an Error Correcting Autoencoder.

We present the use of an error correcting autoencoder stage to a convolutional neural network model as a means of improving image based automatic Posi...

Antibody interface prediction with 3D Zernike descriptors and SVM.

MOTIVATION: Antibodies are a class of proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. This bindin...

Multi-purpose Robotic Training Strategies for Neurorehabilitation with Model Predictive Controllers.

One of the main challenges in robotic neuroreha-bilitation is to understand how robots should physically interact with trainees to optimize motor lean...

A new method using machine learning for automated image analysis applied to chip-based digital assays.

Chip-based digital assays such as the digital polymerase chain reaction (digital PCR), digital loop-mediated amplification (digital LAMP), digital enz...

Semi-supervised learning of a nonnative phonetic contrast: How much feedback is enough?

Semi-supervised learning refers to learning that occurs when feedback about performance is provided on only a subset of training trials. Algorithms fo...

A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.

For deep learning based speech segregation to have translational significance as a noise-reduction tool, it must perform in a wide variety of acoustic...

Deep repeat resolution-the assembly of the Drosophila Histone Complex.

Though the advent of long-read sequencing technologies has led to a leap in contiguity of de novo genome assemblies, current reference genomes of high...

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

PURPOSE: The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early p...

PreMedKB: an integrated precision medicine knowledgebase for interpreting relationships between diseases, genes, variants and drugs.

One important aspect of precision medicine aims to deliver the right medicine to the right patient at the right dose at the right time based on the un...