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MetabolomicsRespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing and agriculture problems. However, the DBTL cycle’s Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing dat...

2023-06-29 | MTBLS3830 | MetaboLights
MetabolomicsWT 2.0: Unveiling the “dark matter” in the metabolome using all-ion stepwise fragmentation acquisition mode (ASFAM) and deep learning-based feature extraction

Current metabolomics methods often miss low-abundance compounds and yield incomplete or ambiguous MS2 spectra, resulting in the presence of “dark matter” within the metabolome. Here, we introduce WT 2.0, which employs an all-ion stepwise fragmentation acquisition mode (ASFAM) to acquire comprehe...

2025-09-23 | MTBLS13023 | MetaboLights
MetabolomicsmassNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation
Not available
ORGANISM(S): Mus musculus 
ST002045 | MetabolomicsWorkbench
ModelsKim2022 - BayeshERG: A deep learning model for predicting hERG channel blockers
BayeshERG is a predictor of small molecule-induced blockade of the hERG ion channel. To increase its predictive power, the authors pretrained a bayesian graph neural network with 300,000 molecules as a transfer learning exercise. The pretraining set was obtained from Du et al, 2015, and the fine tun...
2024-08-06 | MODEL2408060001 | BioModels
Machine Learning Model FAIR-AIML Ersilia 
ModelsKarim2021 - CardioTox net: Ligand-based prediction of hERG blockade
A robust predictor for hERG channel blockade based on an ensemble of five deep learning models. The authors have collected a dataset from public sources, such as BindingDB and ChEMBL on hERG blockers and non-blockers. The cut-off for hERG blockade was set at IC50 Model Type: Predictive machine lea...
2024-07-18 | MODEL2407180003 | BioModels
Machine Learning Model FAIR-AIML Ersilia 
MetabolomicsExperimental and computational approaches for deep metabolome annotation with application to the ecotoxicological model organism Daphnia magna

Identifying metabolomes with greater coverage and confidence is a critical step towards interpreting the metabolic basis of human and environmental health. Yet for over two decades the field of metabolomics has been inhibited by limited metabolite identification. To address this, we developed the...

MTBLS2273 | MetaboLights
ProteomicsDeep learning neural network prediction method improves proteome profiling of vascular sap of grapevines during Pierces disease development
Plant secretome studies have shown the importance of plant defense proteins in the vascular system against pathogens. Studies on Pierces disease of grapevines caused by the xylem-limited bacteria Xylella fastidiosa Xf have detected proteins and pathways associated to its pathobiology. Despite the bi...
ORGANISM(S): Vitis Vinifera (ncbitaxon:29760) Xylella Fastidiosa Temecula1 (ncbitaxon:183190) 
2020-08-12 | MSV000085942 | MassIVE
ModelsWang2022 - Scaffolding protein functional sites using deep learning
Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second appr...
2023-05-10 | BIOMD0000001071 | BioModels
Machine Learning Model FAIR-AIML 
ProteomicsProsit: Proteome-wide prediction of peptide tandem mass spectra by deep learning
In this study, we extended the ProteomeTools peptide library (PROPEL, see PXD004732 and PXD010595) to train a deep neural network resulting in chromatographic retention time and fragment ion intensity predictions for (tryptic) peptides that exceed the quality of the experimental data.
ORGANISM(S): Drosophila Melanogaster (ncbitaxon:7227) Escherichia Coli (ncbitaxon:562) Saccharomyces Cerevisiae (ncbitaxon:4932) Homo Sapiens (ncbitaxon:9606) Caenorhabditis Elegans (ncbitaxon:6239) 
2021-03-12 | MSV000087047 | MassIVE
ProteomicsDeNovo Peptide Identification Deep Learning Training Set
A benchmark set of bottom-up proteomics data for training deep learning networks. It has data from 51 organisms and includes nearly 1 million peptides.
ORGANISM(S): Myxococcus xanthus DZ2 Streptomyces sp. Faecalibacterium prausnitzii SL3/3 Bacteroides fragilis (strain 638R) Clostridium ljungdahlii (strain ATCC 55383 / DSM 13528 / PETC) Francisella tularensis subsp. novicida (strain U112) Algoriphagus marincola HL-49 Chryseobacterium indologenes Cupriavidus necator (strain ATCC 43291 / DSM 13513 / N-1) (Ralstonia eutropha) Citrobacter freundii Stigmatella aurantiaca (strain DW4/3-1) Pseudomonas putida KT2440 Ruminococcus gnavus ATCC 29149 Paenibacillus polymyxa ATCC 842 Bacillus cereus (strain ATCC 14579 / DSM 31) Anaerococcus hydrogenalis DSM 7454 Delftia acidovorans (strain DSM 14801 / SPH-1) Lactobacillus casei subsp. casei ATCC 393 Bacteroides thetaiotaomicron (strain ATCC 29148 / DSM 2079 / NCTC 10582 / E50 / VPI-5482) Cellulophaga baltica 18 Cyanobacterium stanieri Rhodopseudomonas palustris Cellvibrio gilvus (strain ATCC 13127 / NRRL B-14078) Acidiphilium cryptum (strain JF-5) Bacillus subtilis subsp. subtilis str. NCIB 3610 Rhizobium radiobacter (Agrobacterium tumefaciens) (Agrobacterium radiobacter) Sulfobacillus thermosulfidooxidans Dorea formicigenerans Rhodococcus sp. (strain RHA1) Bacillus subtilis subsp. subtilis str. 168 Fibrobacter succinogenes subsp. succinogenes S85 Micrococcus luteus (Micrococcus lysodeikticus) Streptomyces griseorubens Campylobacter jejuni Legionella pneumophila Streptococcus agalactiae Paracoccus denitrificans Mycobacterium smegmatis bacteria Alcaligenes faecalis Listeria monocytogenes serotype 1/2a (strain 10403S) Bifidobacterium bifidum DSM 20456 = JCM 1255 bacteria Coprococcus comes ATCC 27758 bacteria Synechococcus elongatus (strain PCC 7942) (Anacystis nidulans R2) Prevotella ruminicola (strain ATCC 19189 / JCM 8958 / 23) Bifidobacterium longum subsp. infantis (strain ATCC 15697 / DSM 20088 / JCM 1222 / NCTC 11817 / S12) Shewanella oneidensis (strain MR-1) Methylomicrobium alcaliphilum (strain DSM 19304 / NCIMB 14124 / VKM B-2133 / 20Z) 
2018-06-13 | PXD010000 | Pride
Bacterial diversity Machine learning Deep learning 
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