TSTL (Task-Specific Transfer Learning) is introduced as a training strategy for predicting retention times in various LC systems with limited training data. Evaluated across 6 benchmark datasets from different LC systems using 5 deep neural network architectures, TSTL achieved significant improvements in prediction accuracy, increasing average R² from 0.587 to 0.825 with superior data efficiency.

FIDDLE (Formula IDentification by Deep LEarning) is introduced as a deep learning-based method for identifying chemical formulas from MS/MS data. It is trained on over 38,000 molecules and 1 million MS/MS spectra collected under various conditions, including collision energy and precursor types, using Quadrupole Time-of-Flight (QTOF) and Orbitrap instruments.

3DMolCSP leverages a 3D molecular conformation representation algorithm, alongside a dataset of over 300k enantioseparation records. This approach significantly improves enantioselectivity predictions, enabling more efficient and informed decisions in chiral chromatography.

3DMolMS is a deep neural network model that predicts MS/MS spectra from 3D conformations. The learned molecular representation also enhances predictions of chemical properties, such as elution time and collisional cross section, aiding compound identification.

Koina is a user-friendly platform that enables proteomics researchers to apply machine learning without coding expertise. It offers pre-configured workflows for common tasks like, tandem mass spectra, retention time and collisional cross section prediction, along with customizable options for advanced users.

Confounding factors like medications can severely bias microbiome-based disease predictions, leading to spurious associations. This study developed confounder-free models using adversarial optimization to remove biases while preserving true phenotype-microbiome associations. Tested on type 2 diabetes data with metformin as confounder, both FNN_CF and MicroKPNN_CF outperformed conventional approaches by identifying genuine disease markers. MicroKPNN_CF offered superior interpretability despite slightly lower accuracy than FNN_CF.

Metadata like age and gender are often missing in microbiome studies but crucial for accurate disease prediction. MicroKPNN-MT addresses this by either using available metadata as input or predicting it from microbiome profiles. Tested across 25 diseases, the model showed that incorporating real or predicted metadata improves both prediction accuracy and generalizability, making it a practical tool for microbiome-based disease prediction with incomplete data.