Genetics

Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation.

For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic data s...

Spiking Neural P Systems with Delay on Synapses.

Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapses, SN P systems with delay on synapses (SNP-DS systems) are proposed in this work. Unlike the traditional SN P systems, where all the postsynaptic neu...

Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions p...

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. Howev...

Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients.

As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of ...

EDeepSSP: Explainable deep neural networks for exact splice sites prediction.

Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods...

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning.

N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, ...

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the current study, we aimed to predict the genetic infor...

Cell type prioritization in single-cell data.

We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learn...

Cross-species regulatory sequence activity prediction.

Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guide...

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemora...

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large da...

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and acc...

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and ...

Robotic DNA Nanostructures.

Over the past decade, DNA nanotechnology has spawned a broad variety of functional nanostructures tailored toward the enabled state at which applicati...

A secured cryptographic system based on DNA and a hybrid key generation approach.

Cryptography is a method for preventing illegitimate access to information and data. In this paper, a bio-inspired cryptographic DNA system has been p...

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gen...

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wr...

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a co...

Rapid diagnosis of hereditary haemolytic anaemias using automated rheoscopy and supervised machine learning.