Pattern proline rich linear motifs are excellent mediators

Pattern recognition is one of the key strategies by
which brain performs analogical reasoning of many life problems based on the
information accumulated through its sense organs.  In a general perspective, pattern recognition
involves receiving an input data, analysing it for similar, specific, regular
patterns based on which meaningful interpretation is made. Pattern recognition
can be employed to make computers execute tasks like humans, even faster and
more accurately, by figuring out actual problems and using a collection of
mathematical, statistical, heuristic and inductive techniques to find solutions.
When a computer program is trained to learn the pattern and categorize the data,
then it is machine learning or machine pattern recognition.  Solutions based on pattern recognition may be
employed almost everywhere and anywhere – medicine, health and pharma industry,
agriculture, financial markets, forensic investigations. During the last few
decades, enormous amount of biological data in different formats has been
generated using advanced technologies. Moreover, number of databases is also
developed by researchers, which accumulates huge molecular data. Consequently,
demand for new computational techniques is also increased for better processing
of this data.  Mining useful biological
information from this data and analysing it has become an important task since
it give insights into the underlying functionalities and cellular processes. The
highly complex nature of biological data demands development of good quality
pattern recognition algorithms to ensure reliable output.

Studies reveal that biomolecules (DNA, RNA, proteins) in
sequence and structural form contain different patterns that are functionally
relevant. These patterns also known as motifs are very much involved in the
characterization of these biomolecules. In proteins, patterns may also occur
for which the elements found in secondary structure. Helix turn helix is a
widely studied motif that falls in the category of DNA binding motifs.
Detection techniques of such patterns make use of the structural features as
well 1. Recent studies in drug discovery show that proline rich linear motifs
are excellent mediators for intermolecular interactions seen in many faces of
immune response activities, and hence these motifs are considered as drug
targets in immune mediated diseases. Alignment method, local search, heuristic
approach etc. are a few among the applied techniques for this pattern
identification task. Within medical science, pattern recognition is the basis
for computer-aided diagnosis (CAD) systems. CAD describes a procedure that
supports the doctor’s interpretations and findings. Detection of patterns
demands computational techniques that produce optimum results. 

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