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Proteomics

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Identifying the missing proteins in human proteome by biological language model.

BMC systems biology
BACKGROUND: With the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and p...

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BMC bioinformatics
BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by s...

Apoptosis of rat hepatic stellate cells induced by diallyl trisulfide and proteomics profiling in vitro.

Canadian journal of physiology and pharmacology
Diallyl trisulfide (DATS), a major garlic derivative, inhibits cell proliferation and triggers apoptosis in a variety of cancer cell lines. However, the effects of DATS on hepatic stellate cells (HSCs) remain unknown. The aim of this study was to ana...

Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

Journal of proteome research
A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present a dynamic Bayesian network (DBN) toolkit that addresses this problem by using a machine lea...

Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.

PloS one
Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent ...

Improving Protein Expression Prediction Using Extra Features and Ensemble Averaging.

PloS one
The article focus is the improvement of machine learning models capable of predicting protein expression levels based on their codon encoding. Support vector regression (SVR) and partial least squares (PLS) were used to create the models. SVR yields ...

UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.

BMC systems biology
BACKGROUND: The conjugation of ubiquitin to a substrate protein (protein ubiquitylation), which involves a sequential process--E1 activation, E2 conjugation and E3 ligation, is crucial to the regulation of protein function and activity in eukaryotes....

Deep Learning in Drug Discovery.

Molecular informatics
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research b...

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

PloS one
We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (Gene...

l2 Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification.

IEEE/ACM transactions on computational biology and bioinformatics
SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental reposi...