AIMC Topic: Software

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SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may...

[Comparative study of two software for the detection of cephalometric landmarks by artificial intelligence].

L' Orthodontie francaise
INTRODUCTION: Manual, tedious cephalometric analyzes of a lack of productivity (errors in plotting and measurement) making the prospect of a fully automated algorithm turning out attractive. The objectives of the study were to evaluate the positionin...

[Deep learning-assisted construction of three-demensional facial midsagittal plane].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To establish a deep learning algorithm that can accurately determine three-dimensional facial anatomical landmarks, multi-view stacked hourglass convolutional neural networks (MSH-CNN) and to construct three-dimensional facial midsagittal ...

Mathematical foundations based statistical modeling of software source code for software system evolution.

Mathematical biosciences and engineering : MBE
Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the ...

Virtifier: a deep learning-based identifier for viral sequences from metagenomes.

Bioinformatics (Oxford, England)
MOTIVATION: Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from m...

Automated classification of cytogenetic abnormalities in hematolymphoid neoplasms.

Bioinformatics (Oxford, England)
MOTIVATION: Algorithms for classifying chromosomes, like convolutional deep neural networks (CNNs), show promise to augment cytogeneticists' workflows; however, a critical limitation is their inability to accurately classify various structural chromo...

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression ...

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to ...

Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is...

Explainable multimodal machine learning model for classifying pregnancy drug safety.

Bioinformatics (Oxford, England)
MOTIVATION: Teratogenic drugs can cause severe fetal malformation and therefore have critical impact on the health of the fetus, yet the teratogenic risks are unknown for most approved drugs. This article proposes an explainable machine learning mode...