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A genetic similarity algorithm is introduced in this study to find a group of semantically similar Gene Ontology terms. The genetic similarity algorithm combines semantic similarity measure algorithm with parallel genetic algorithm. The semantic similarity measure algorithm is used to compute the similitude strength between the Gene Ontology terms. Then, the parallel genetic algorithm is employed to perform batch retrieval and to accelerate the search in large search space of the Gene Ontology graph. The genetic similarity algorithm is implemented in the Gene Ontology browser named basic UTMGO to overcome the weaknesses of the existing Gene Ontology browsers which use a conventional approach based on keyword matching. To show the applicability of the basic UTMGO, we extend its structure to develop a Gene Ontology -based protein sequence annotation tool named extended UTMGO. The objective of developing the extended UTMGO is to provide a simple and practical tool that is capable of producing better results and requires a reasonable amount of running time with low computing cost specifically for offline usage. The computational results and comparison with other related tools are presented to show the effectiveness of the proposed algorithm and tools.

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In the last decade, the emergence of diverse students in a streamed class based on the mathematical ability brought upon challenges for the teacher to find an approach which could accommodate all students. This study aims to investigate the connection of students' ability with their learning preferences in mathematics by applying differentiated instruction using tiered assignments and parallel tasks. This qualitative approach was used for this study from a sample of year 7 mathematics students within Pengukuhan Kemahiran Asas programme (PeKA) (the Basic Skills Strengthening programme), which revolved upon low ability for students with very weak numeracy skills. The findings in general revealed that there is an improvement in their performance.