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The simulation experiment description markup language (SED-ML): language specification for level 1 version 4.

Journal of integrative bioinformatics
Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive co...

Identification of Gene Regulatory Networks from Single-Cell Expression Data.

Methods in molecular biology (Clifton, N.J.)
Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions...

celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition.

PloS one
Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell dea...

Universal probabilistic programming offers a powerful approach to statistical phylogenetics.

Communications biology
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabi...

Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.

Journal of chemical theory and computation
Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates...

A weighted patient network-based framework for predicting chronic diseases using graph neural networks.

Scientific reports
Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We...

GapPredict - A Language Model for Resolving Gaps in Draft Genome Assemblies.

IEEE/ACM transactions on computational biology and bioinformatics
Short-read DNA sequencing instruments can yield over 10 bases per run, typically composed of reads 150 bases long. Despite this high throughput, de novo assembly algorithms have difficulty reconstructing contiguous genome sequences using short reads ...

OCTID: a one-class learning-based Python package for tumor image detection.

Bioinformatics (Oxford, England)
MOTIVATION: Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not....

SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications.

BMC bioinformatics
BACKGROUND: Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate r...

Deep learning-enhanced, open-source eigenmode expansion.

Optics letters
We present an open-source eigenmode expansion (EME) software package entirely implemented in the Python programming language. Eigenmode expansion Python (EMEPy) utilizes artificial neural networks to reproduce electromagnetic eigenmode field profiles...