Computational Insights into Gene Networks
Enhanced computational techniques to aid in understanding gene interactions
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This book describes novel computational techniquesthat are used in predicting gene interactions usingtwo main online resources: biomedical literature andsequence data. The co-occurrence method of textmining was extensively explored in this book. Theeffects of certain parameters on influencing therelevancy of documents in which two genes co-occurwere critically examined. The results showed thatindeed some parameters, if taken into consideration,can lead to better identification of documents thatdescribe gene interactions. A system was developed aspart of the work and can be obtained by contacti...
This book describes novel computational techniques
that are used in predicting gene interactions using
two main online resources: biomedical literature and
sequence data. The co-occurrence method of text
mining was extensively explored in this book. The
effects of certain parameters on influencing the
relevancy of documents in which two genes co-occur
were critically examined. The results showed that
indeed some parameters, if taken into consideration,
can lead to better identification of documents that
describe gene interactions. A system was developed as
part of the work and can be obtained by contacting
the author: mustafa.dameh@gmail.com. The book also
describes a novel approach that used an analysis of
SAGE and EST sequence libraries using ecological
estimation methods. The approach proves that species
accumulation theory used in ecology can be applied to
tag libraries to predict an upper bound to the number
of mRNA transcript species in a tissue. The novel
computational analysis provided in this book can be
used to extend the body of knowledge and insights
relating to gene interactions and, hence, provide
better understanding of genes and their functions
within gene networks.
that are used in predicting gene interactions using
two main online resources: biomedical literature and
sequence data. The co-occurrence method of text
mining was extensively explored in this book. The
effects of certain parameters on influencing the
relevancy of documents in which two genes co-occur
were critically examined. The results showed that
indeed some parameters, if taken into consideration,
can lead to better identification of documents that
describe gene interactions. A system was developed as
part of the work and can be obtained by contacting
the author: mustafa.dameh@gmail.com. The book also
describes a novel approach that used an analysis of
SAGE and EST sequence libraries using ecological
estimation methods. The approach proves that species
accumulation theory used in ecology can be applied to
tag libraries to predict an upper bound to the number
of mRNA transcript species in a tissue. The novel
computational analysis provided in this book can be
used to extend the body of knowledge and insights
relating to gene interactions and, hence, provide
better understanding of genes and their functions
within gene networks.
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