  Systematic identification of protein function is a key problem in current
biology. Most traditional methods fail to identify functionally equivalent
proteins if they lack similar sequences, structural data or extensive manual
annotations. In this thesis, I focused on feature engineering and machine
learning methods for identifying diverse classes of proteins that share
functional relatedness but little sequence or structural similarity, notably,
Neuropeptide Precursors (NPPs).
  I aim to identify functional protein classes solely using unannotated protein
primary sequences from any organism. This thesis focuses on feature
representations of whole protein sequences, sequence derived engineered
features, their extraction, frameworks for their usage by machine learning (ML)
models, and the application of ML models to biological tasks, focusing on high
level protein functions. I implemented the ideas of feature engineering to
develop a platform (called NeuroPID) that extracts meaningful features for
classification of overlooked NPPs. The platform allows mass discovery of new
NPs and NPPs. It was expanded as a webserver.
  I expanded our approach towards other challenging protein classes. This is
implemented as a novel bioinformatics toolkit called ProFET (Protein Feature
Engineering Toolkit). ProFET extracts hundreds of biophysical and sequence
derived attributes, allowing the application of machine learning methods to
proteins. ProFET was applied on many protein benchmark datasets with state of
the art performance. The success of ProFET applies to a wide range of
high-level functions such as metagenomic analysis, subcellular localization,
structure and unique functional properties (e.g. thermophiles, nucleic acid
binding).
  These methods and frameworks represent a valuable resource for using ML and
data science methods on proteins.
