Genetics

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning.

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction ...

ChrNet: A re-trainable chromosome-based 1D convolutional neural network for predicting immune cell types.

Cells from our immune system detect and kill pathogens to protect our body against various diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput, etc. Immune cells that are associated with cancer tissues play a critical role in revealing tumor development. Identifying the immune composition within tumor micro...

Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer.

BACKGROUND: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors...

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the o...

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.

Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have...

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning.

A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and the...

Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections.

The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their d...

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data.

Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essentia...

Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of...

Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques.

BACKGROUND/AIMS: To investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict t...

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.

To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the a...

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly diff...

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate anima...

Iterative single-cell multi-omic integration using online learning.

Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approa...

An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods.

Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species ...

A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements.

Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improv...

Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning.

An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron mic...

CBCR: A Curriculum Based Strategy For Chromosome Reconstruction.

In this paper, we introduce a novel algorithm that aims to estimate chromosomes' structure from their Hi-C contact data, called Curriculum Based Chrom...

Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.

Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In additi...

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network an...