cent advances in Bioinformatics and Computational Biology

underlingbuddhaΒιοτεχνολογία

2 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

81 εμφανίσεις

The

completion

of

the

Fugu

genome

marked

an

important

event

for

bioinformatics
:

the

completion

of

the

first

of

many

vertebrate

genomes

to

be

studied

after

the

human

genome

was

unveiled

in

2001

which

in

turn

has

opened

the

doors

to

comparative

genomics
.

In

this

talk

I

will

discuss

our

work

on

the

Fugu

genome

as

well

as

on

comparative

genomics

in

the

wider

sense,

the

informatics

challenges

that

it

poses

as

well

as

the

biological

discoveries

it

facilitates


Introduction to BIRC Research

A/P
Jagath C. Rajapakse


Deputy Director, BIRC
,
Nanyang Technological University

Recent Advances in Bioinformatics and Computational Biology


8 March, 2.00pm
-

5.00pm LT8, Level 2, North Spine

13:45

Registration



14:00

Introduction to BIRC Research


A/P
Jagath C. Rajapakse



Deputy Director, BIRC


Nanyang Technological University

14:10

Some Sample Problems and
Solutions in Post
-
Genome
Knowledge Discovery


A/P Limsoon Wong


Institute for Infocomm Research


14:40

The Fugu Genome at the Verge of a
New Bioinformatics Explosion


Mr Elia Stupka



Fugu Informatics, IMCB



15:10

Refreshments



15:30

Getting Your Data
-
Driven Life
Sciences Research Up and Running


Mr Amey V. Laud



HeliXense

Pte Ltd

16:00

Applications of Metaheuristics in
Bioinformatics



Dr Kuo
-
Bin Li


BioInformatics Institute

16:30

Multimodality as a Criterion for
Feature Selection in Unsupervised
Analysis on Gene Expression Data


Dr Li Yi


Genomics Institute of Singapore



17:00

End



Free Admission All are Welcome


The Fugu Genome at the Verge of

a New Bioinformatics Explosion

Mr Elia Stupka


Fugu Informatics, IMCB



-

Organised by :

BioInformatics Research Centre

Applications of Metaheuristics in Bioinformatics


Dr Kuo
-
Bin Li

BioInformatics Institute

COE Technology Week 2002 Focus Seminar

Research

at

BIRC

aims

at

the

design

and

development

of

algorithms

and

tools

to

store,

analyze,

and

visualize

biological

data
.

Current

research

projects

are

in

structural

and

functional

genomics,

neuroinformatics

and

medical

informatics,

data

visualization,

mining,

and

integration,

and

grid

computing
.

This

talk

will

briefly

outline

some

projects

presnetly

carried

out

at

BIRC



Many

bioinformatics

applications

involve

combinatorial

search

over

a

large

solution

space
.

For

example,

multiple

sequence

alignment

whose

aim

is

to

find

the

optimal

alignment

of

a

group

of

nucleotide

or

protein

sequences

is

a

combinatorial

optimization

problem
.

Metaheuristics

are

approaches

that

guide

local

heuristic

search

procedure

to

explore

the

solution

space

beyond

local

optimality
.

Examples

of

metaheuristics

include

genetic

algorithm,

simulated

annealing

and

tabu

search
.

With

the

advent

of

powerful

distributed

or

parallel

computers,

new

bioinformatics

algorithms

making

use

of

metaheuristics

will

hopefully

be

able

to

produce

quality

results

within

reasonable

amount

of

time
.

A

few

recent

applications

will

be

discussed
.


Some Sample Problems and Solutions in

Post
-
Genome Knowledge Discovery

A/P Limsoon Wong

Institute for Infocomm Research

Informatics

has

helped

in

launching

molecular

biology

into

the

genomic

era
.

It

appears

certain

that

informatics

will

continue

to

be

a

major

factor

in

the

success

of

molecular

biology

in

the

post
-
genome

era
.

In

this

talk,

we

describe

advances

made

in

data

mining

technologies

that

are

relevant

to

molecular

biology

and

biomedical

sciences
.

In

particular,

we

discuss

some

recent

research

results

on

topics

such

as

(a)

the

prediction

of

immunogenic

peptides,

(b)

the

discovery

of

gene

structure

features,

(c)

the

classification

of

gene

expression

profiles,

and

(d)

the

extraction

of

protein

interaction

information

from

literature
.


Multimodality as a Criterion for

Feature Selection in Unsupervised Analysis on

Gene Expression Data

Dr Li Yi

Genomics Institute of Singapore

One

important

way

that

gene

expression

data

is

often

analyzed

is

to

cluster

the

samples

without

reference

to

any

annotation

about

them
.

Before

clustering,

the

data

is

often

subjected

to

a

feature

selection

preprocessing

step,

in

which

a

subset

of

genes

is

chosen

for

further

analysis
.

We

examine

the

use

of

multimodality

as

a

criterion

for

choosing

genes

in

feature

selection,

and

compare

its

use

with

variance,

which

is

more

commonly

used

at

present
.

Both

are

compared

when

used

in

conjunction

with

an

algorithm

that

clusters

the

samples

in

different

ways,

based

on

different

subsets

of

the

genes
.

The

key

idea

of

this

algorithm

is

to

cluster

genes

using

as

a

similarity

measure

the

mutual

information

between

partitions

on

the

samples

obtained

by

clustering

the

samples

using

the

individual

genes

being

compared
.