In our algorithm, the occluded pixels of target object can be represented by the trivial templates, so when partial occlusion occurs, our tracker is more robust than the IVT tracker.
A virtual screening against m multiple targets can be represented as a set D of l labeled fingerprints of compounds ((mathbf {x}_{i},y_{i1},cdots,y_{textit {ik}},cdots,y_{textit {im}}), i = 1,cdots,l,k = 1,cdots,m,mathbf {x}_{i} in mathbb {R}^{n}, y_{textit {ik}} in {0,1} ).
The i th frame of the target video can be represented as follows: (2).
Both the state and target distributions can be represented by either a parameterized distribution (e.g. a normal distribution with specific mean and variance) or discretized to form a histogram.
The proposed method can be used, as soon as drug molecules and target proteins can be represented by descriptors (chemical substructures and protein domains in this study).
Hence, a cell population expressing an shRNA library against a target gene can be represented as a continuous distribution comprising multiple samples.
The signal response to increasing binding of fluorescent labeled target molecules can be represented as a binding curve and this has been shown with dilution experiments in Ramdas et. al [ 17].
Drug-Target Network Projection using Network-Based Inference A drug-target interaction can be represented as a bipartite graph ( Gleft( {D,T,E} right) ), where drug set ( D = left{ {d_{1},d_{2}, ldots,d_{m} } right} ), target set ( T = left{ {t_{1},t_{2}, ldots,t_{n} } right} ) and ( E = e_{ij} :t_{i} T,d_{j} D ).
These techniques capitalize on bioactivity data to infer relationships between the compounds, encoded with numerical descriptors, and their targets, which can be represented as labels in a classification model or explicitly encoded by e.g. protein or amino acid descriptors [4].
On the other hand, the proposed method can provide the optimal estimation results if the target local patches can be represented in the obtained eigenspaces, correctly.
