Genetics

Identifying tumor cells at the single-cell level using machine learning.

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencin...

Programming Molecular Systems To Emulate a Learning Spiking Neuron.

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and, as such, does not require feedback, making it suitable in contexts where systems have to learn autonomously. This paper explores how molecular systems can be designed to show such protointelligent behaviors and propos...

Rewritable two-dimensional DNA-based data storage with machine learning reconstruction.

DNA-based data storage platforms traditionally encode information only in the nucleotide sequence of the molecule. Here we report on a two-dimensional...

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mut...

Innovating Pedagogical Practices for Handmade Courses in Preschool Education Using Artificial Intelligence.

Handmade is an important part of preschool education, which was aimed at improving children's ability to work with their hands. Preschool education is...

Metabonomic and transcriptomic analyses of glycosides tablet-induced hepatotoxicity in rats.

We aimed to explore novel biomarkers involved in alterations of metabolism and gene expression related to the hepatotoxic effects of glycosides table...

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these as...

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.

Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout th...

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired so...

Identification of DNA N4-methylcytosine sites based on multi-source features and gradient boosting decision tree.

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replicat...

Prediction of nucleosome dynamic interval based on long-short-term memory network (LSTM).

Nucleosome localization is a dynamic process and consists of nucleosome dynamic intervals (NDIs). We preprocessed nucleosome sequence data as time ser...

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI...

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches.

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has ...

Automatic recognition of micronucleus by combining attention mechanism and AlexNet.

BACKGROUND: Micronucleus (MN) is an abnormal fragment in a human cell caused by disorders in the mechanism regulating chromosome segregation. It can b...

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computa...

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diag...

Editorial for "A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Prediction of KRAS Mutation in Rectal Cancer".

Advances in Designer DNA Nanorobots Enabling Programmable Functions.

The advent of DNA nanotechnology has paved the way for the development of nanoscale robotics capable of executing smart and sophisticated tasks in a p...

Machine learning sequence prioritization for cell type-specific enhancer design.

Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within het...

Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: A systematic review and meta-analysis.

PURPOSE: The study aimed to determine the diagnostic yield, optimal timing, and methodology of next generation sequencing data reanalysis in suspected...