Genetics

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications.

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogeni...

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been ...

Using Machine Learning for Predicting the Effect of Mutations in the Initiation Codon.

The effect of mutations has been traditionally predicted by studying what may happen due to the substitution of one amino acid for another one. This a...

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have...

Systematic tissue annotations of genomics samples by modeling unstructured metadata.

There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discoverin...

Pharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases.

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper devel...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening m...

Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade.

Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligan...

Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering...

A novel deep learning-based method for COVID-19 pneumonia detection from CT images.

BACKGROUND: The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of CO...

Revealing low-temperature plasma efficacy through a dose-rate assessment by DNA damage detection combined with machine learning models.

Low-temperature plasmas have quickly emerged as alternative and unconventional types of radiation that offer great promise for various clinical modali...

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.

METHODS: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed ...

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdeterm...

Current understanding of biological interactions and processing of DNA origami nanostructures: Role of machine learning and implications in drug delivery.

DNA origami has emerged as an exciting avenue that provides a versatile two and three-dimensional DNA-based platform for nanomedicine and drug deliver...

D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.

The number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial expl...

LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome.

DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there a...

Pandemic disease detection through wireless communication using infrared image based on deep learning.

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain rea...

Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level.

Melanoma is an aggressive and metastatic skin cancer caused by genetic mutations in melanocytes, and its incidence is increasing year by year. Underst...

Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models.

Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationshi...

Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response.

PURPOSE: To investigate the effects of deep learning-based imaging reconstruction (DLR) on the image quality of MRI of rectal cancer after chemoradiot...