Keywordsproteins; position-specific scoring matrix; probabilistic classification vector machines; uveitis; protein–protein interaction; random walk with restart algorithm; Bacillus; glucose 1-dehydrogenase; acid-resistant; thermal-stable; molecular dynamics simulation; Tetranychus cinnabarinus; plasma membrane Ca2+-ATPase; scopoletin; coumarin derivatives; molecular docking; three-dimensional quantitative structure activity relationship (3D-QSAR); interaction mechanism; Adenosine 5′-monophosphate-activated protein kinase; virtual screening; molecular docking; selective activator; Salmonella; biocide; serum; antimicrobial resistance; molecular biology; outer membrane protein analysis; random projection; hot spots; IBk; ensemble system; drug–target interactions; discrete wavelet transform; network property; support vector machine; insulin-like growth factor binding protein (IGFBP); insulin-like androgenic gland peptide (IAG); insulin-like peptides (ILP1; ILP2); molecular modelling; binding interaction; alanine scanning; hotspot residue; electrostatics; decapod; ion channels; pseudo-dipeptide composition; machine learning method; PSFM-DBT; DNA binding protein; distance bigram transformation; PSFM; biological networks; cluster analysis; cytoscape; visualization; protein–protein interaction network; overlapping; clustering; amyloid; Waltz; SARP; plant; prion; seed storage protein; proteomics; compositionally biased region; amyloidogenic region; protein-protein interactions; amino acid sequences; local conjoint triad descriptor; deep neural networks; pseudo-amino acid compositions; pseudo-k nucleotide compositions; extensible software; protein subcellular localization; kernel parameter selection; kernel discriminant analysis (KDA); Gaussian kernel function; dimension reduction; false discovery rate; machine learning; protein function prediction; support vector machine; BLAST; bioinformatics; machine learning; feature selection; protein classification; network analysis; molecular docking
