Genetics

Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors.

Chromosome aberration (CA) is a serious genotoxicity of a compound, leading to carcinogenicity and developmental side effects. In the present manuscript, we developed a QSAR model for CA prediction using artificial intelligence methodologies. The reliable QSAR model was constructed based on an enlarged data set of 3208 compounds by optimizing machine learning and deep learning algorithms based on ...

An integrated network representation of multiple cancer-specific data for graph-based machine learning.

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic informati...

Classification of non-coding variants with high pathogenic impact.

Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribut...

Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.

This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) ap...

Building a model for predicting metabolic syndrome using artificial intelligence based on an investigation of whole-genome sequencing.

BACKGROUND: The circadian system is responsible for regulating various physiological activities and behaviors and has been gaining recognition. The ci...

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning.

Surgical margin analysis (SMA), an essential procedure to confirm the complete excision of cancerous tissue in tumor resection surgery, requires intra...

Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species.

DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, ...

MTS-Stega: Linguistic Steganography Based on Multi-Time-Step.

Generative linguistic steganography encodes candidate words with conditional probability when generating text by language model, and then, it selects ...

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of the...

DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning.

RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis...

Artificial intelligence to identify genetic alterations in conventional histopathology.

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular ...

One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.

Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different ...

Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms.

Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging...

Machine learning models identify gene predictors of waggle dance behaviour in honeybees.

The molecular characterization of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed ...

Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis.

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual ...

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parall...

FCNGRU: Locating Transcription Factor Binding Sites by Combing Fully Convolutional Neural Network With Gated Recurrent Unit.

Deciphering the relationship between transcription factors (TFs) and DNA sequences is very helpful for computational inference of gene regulation and ...

Nucleosome positioning based on DNA sequence embedding and deep learning.

BACKGROUND: Nucleosome positioning is the precise determination of the location of nucleosomes on DNA sequence. With the continuous advancement of bio...

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer.

Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription-polymer...

Chromatin interaction-aware gene regulatory modeling with graph attention networks.

Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and ...