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Applications of Neural Networks
in Biology and Agriculture
Jianming Yu
Department of Agronomy and Plant Genetics
 Introduction to Neural Networks
 Applications of Neural Networks in
Biology and Agriculture
Girl
Boy
How Can We Recognize a Given
Schematic Face?
It is a Boy? Or a Girl?
That Is What A
Neural Network
Is All About!
INFORMATION
INFORMATION
INFORMATION
UNDERSTANDING
INFORMATION
INFORMATION
INFORMATION
UNDERSTANDING
INFORMATION
INFORMATION
INFORMATION
UNDERSTANDING
Girls
+1,+1,+1,-1,-1
+1,-1,+1,+1,+1
-1,+1,-1,+1,+1
-1,-1,+1,-1,-1
-1,+1,-1,+1,-1
+1,-1,+1,-1,-1
Boys
Introduction of Neural Network
•
•
•
•
•
•
Structure of a Neuron/Node
Analogy of Neural Networks
Definition
Architecture
Learning Process
Constructing a Neural Network
Biological vs. Artificial
Human Brain Neural Network
Neurons
Nodes
Dendrites
Inputs/Sensors
Axons
Outputs
Synapses
Weights
Information procession
Learning by examples
Generalize beyond examples
What is a Neural Networks?
Hidden Layer
weights
Input Layer
Output Layer
Architecture of Neural Networks
Perceptron
Feed-forward
Feedback
Learning Process
• Supervised learning
– Back-propagation algorithm
• Least Mean Square Convergence
If output too small, + / - unit weights
If output too large, - / + unit weights
-1.00
W1 = 0.3
-0.25
W2 = 0.2
Network
Output
-0.50
0.50
-1.00
W4 = - 0.5
Knowledge/
Weight Matrix
W1 = 0.2
-0.25
W2 = 0.2
0.2
0.2
-0.45
-1.00
-0.45
W3 = 0.5
-1.00
0.50
Known
Output
W3 = 0.4
W4 = - 0.5
-0.45
0.4
-0.5
Learning Process
• Supervised learning
• Unsupervised learning
Constructing a Supervised
Neural Network
• Determine architecture
• Set learning parameters & initialize
weights.
• Code the data
• Train the network
• Evaluate performance
Applications of Neural Networks
•
General Information
1. Search for a gene
2. Gene expression network
3. Kernel number prediction
Applications of Neural Network
• Pattern classification
• Clustering
• Forecasting and prediction
• Nonlinear system modeling
• Speech synthesis and recognition /
Function approximation / Image
compression / Combinational optimization
•
Business
–
Forecast the maximum return configuration
of a stock portfolio
–
Credit risk analysis
–
Forecasting airline passenger booking
• Medicine
– Diagnosing the cardiovascular system
– Electronic noses
– Instant Physician
Dr. Computer:
You have diabetes.
Please …
Example 1
Coding Region Recognition
and Gene Identification
ATGCATATCGCACTATAGCCGCCCCGACATAGCCGCAAAT
Example 1
• Sensors
– Codon usage, base composition,
periodicity, splice site, coding 6-tuples,
etc.
• Training Data
– Known genes
• Validating Data
– Known genes
• Objective
– Find genes in unannotated sequence
Example 1
Uberbacher, E. C. et al., 1991. Locating protein-coding
regions in human DNA sequences by a mulitple
sensor-neural network approach. PNAS. 88, 1126111265.
Sensors
Discrete exon score
Example 1
• Human, Mouse, Arabidopsis,
Drosophilae, E.coli
• GRAIL / GRAIL EXP
• http://compbio.ornl.gov/
Example 1
“With modest effort, an investigator can
greatly enrich the value of the sequence
under study by including descriptions of
the genes, proteins, and regulatory
regions that are present. Such analysis
will provide a starting point to this most
exciting phase of genome research”
--Uberbacher, 1996
Example 2
Gene Expression Networks
Off
On
On
Example 2
• Sensors
– Temperature, Day length
• Training Data
– Experiment
• Validating Data
– Molecular Maker, Microarray,
Experiment
• Objectives
– Simulate the gene expression network
– Test the developmental gene hierarchy
Example 2
Welch, S. M., et al. 2000. Modeling the Genetic
Control of Flowering in Arabidopsis thalina. J. of
Agro.
Arabidopsis thalina wildtype
Temp
Flower
Day length
Example 2
PHYB
CRY2
GI
FPA
Temp
Day length
FVE
CO
Integration
FCA
Flower
Example 2
light/dark
16 oC
20 oC
24 oC
8/16 hr
Run 4
Run 6
Run 3
16/ 8 hr
Run 2
Run 5
Run 1
Fraction Transition
Fit at 24oC
1
0.8
0.6
Ler Wildtype
0.4
CO
FVE
0.2
0
0
5
10
15
20
25
30
35
40
Days
Fraction Transition
Fit at 16oC
1
0.8
0.6
Ler Wildtype
FVE
CO
40
50
0.4
0.2
0
0
10
20
30
Days
60
70
Example 2
• Neural networks can be employed to
model the genetic control of plant process
• Nodes can be linked in one-one
correspondence with networks
constructed by genomic techniques
• Complex phenotypic behavior can be
related to internal network characteristics
• GxE less readily mimicked by existing
models
Example 3
Example 3
Kernel Number Prediction
Example 3
• Sensors
Biomass: Total biomass produced during the
critical period of ear elongation
Population: Plant Population
• Training Data
– Experiment
• Validating Data
– Experiment
• Objective
– Predict the Kernel Number
Example 3
Dong, Zhanshan, et al. 2000. A Neural Network
Model for Kernel Number of Corn -- Training and
Representing in STELLA.
Corn Field
Biomass
Population
Kernel
Number
Example 3
Example 3
• Corn Cultivar : Medium maturity
• Automatic irrigation
• Data for training
• 10 years in Manhattan, KS (1990-1999)
• 5 years in Parsons, KS (1995-1999)
• Data for validation
• 2 years in Manhattan, KS (1985-1986)
Validation Data
Example 3
* Actual KN
 Predicted KN
Kernel number
800
600
400
120
200
100
0
12
10
80
8
Biomass
6
Plant Population
4
60
700
650
Example 3
Predicted KN vs. Actual KN
KNPred=30.651+0.9321*KNActual
600
Predicted KN
550
500
450
400
350
* Validation data
o Training data
300
250
200
200
300
400
500
Actual KN
600
700
Example 3
• Training a neural network that can
effectively simulate kernel number of corn
needs a wild range of data set
• Neural network can simulate kernel
number of corn by using total biomass
produced during the critical period of ear
elongation and plant population
• STELLA can represent the neural network
easily and efficiently
Strengths
• Knowledge not needed (?)
• Can handle complex problems
• Fairly fast run time looks at all the
information at once
• Can deal with noisy and incomplete data (?)
• Adaptability over time, continuous learning.
Weaknesses
• Cannot separate correlation and causality
• No explanation or justification facilities
• Weights don’t have obvious interpretations (?)
• No confidence intervals (?)
• Requires lots of data and training time
If you want to know more about
Neural Network,
• http://www.doc.ic.ac.uk/~nd/surprise_96/jo
urnal/vol4/cs11/report.html
• Neural Networks, by Herve Abdi, et al.,
1999.
• Neural Networks and Genome Informatics,
by C. H. Wu, and McLarty, J. W., 2000
Acknowledgement
• Dr. Rex Bernardo
• Dr. Nevin Young
• Dr. JoAnna Lamb, Jimmy Byun, Bill Peters,
Marcelo Pacheco, Bill Wingbermuehle, Luis
Moreno-Alvarado, Ebandro Uscanga-Mortera
• APS and Agronomy Dept.
• Dr. S. M. Welch, Zhanshan Dong, and Yuwen
Zhang. (K-State)
Research & Interest of Neural Network
?
“Neural networks do not perform
miracles. But if used sensibly they can
produce some amazing results.”
-- C. Stergiou and D. Siganos
Time
1940
1960 19691970 1980
1990
2000