AIMC Topic: Computational Biology

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A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising.

Computational and mathematical methods in medicine
In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed b...

A recurrent neural network framework for flexible and adaptive decision making based on sequence learning.

PLoS computational biology
The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is anal...

Prediction of cancer dependencies from expression data using deep learning.

Molecular omics
Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply...

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Trends in pharmacological sciences
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques...

Transfer learning enables prediction of CYP2D6 haplotype function.

PLoS computational biology
Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug ...

Learning and interpreting the gene regulatory grammar in a deep learning framework.

PLoS computational biology
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features...

Protein molecular defect detection method based on a neural network algorithm.

Cellular and molecular biology (Noisy-le-Grand, France)
Proteins, as the largest macromolecules in the body, are among the most important components of the body and play very vital and important roles. These substances are made up of a series of amino acid chains that, depending on the type of protein, th...

Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions.

Cell host & microbe
Glycans, the most diverse biopolymer, are shaped by evolutionary pressures stemming from host-microbe interactions. Here, we present machine learning and bioinformatics methods to leverage the evolutionary information present in glycans to gain insig...

Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.

Computational and mathematical methods in medicine
The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can re...

Automatic Gene Function Prediction in the 2020's.

Genes
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active...