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!

INFORMATIONINFORMATIONINFORMATION

INFORMATIONINFORMATIONINFORMATION

INFORMATIONINFORMATIONINFORMATION

UNDERSTANDING

UNDERSTANDING

UNDERSTANDING

+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

Girls

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 NetworkNeurons NodesDendrites Inputs/SensorsAxons OutputsSynapses Weights

Information procession

Learning by examplesGeneralize beyond examples

What is a Neural Networks?

weights

Output Layer

Hidden Layer

Input Layer

Architecture of Neural Networks

Feed-forward Feedback

Perceptron

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

-0.25

0.50

-1.00

W1 = 0.3

W2 = 0.2

W4 = - 0.5

W3 = 0.5

-0.50 -0.45

-1.00

-0.25

0.50

-1.00

W1 = 0.2

W2 = 0.2

W4 = - 0.5

W3 = 0.4

-0.45 -0.45

Known Output

Network Output

0.2

0.2

0.4

-0.5

Knowledge/ Weight Matrix

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

• 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, 11261-11265.

Discrete exon scoreSensors

Example 1

• Human, Mouse, Arabidopsis, Drosophilae, E.coli

• GRAIL / GRAIL EXP

• http://compbio.ornl.gov/

Example 1

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

On

Off

On

• 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.

Temp

Day length

Flower

Arabidopsis thalina wildtype

Example 2

FPA

CRY2

GI

CO

PHYB

FVE FCA

Integration

Temp

Day length

Flower

Example 2

In-Tray Temps

Tray Layout

PlantingPattern

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

Example 2

Fit at 24oC

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 40

Days

Fra

cti

on

Tra

ns

itio

n

Ler Wildtype CO FVE

Fit at 16oC

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70

Days

Fra

cti

on

Tra

ns

itio

n

Ler Wildtype FVE CO

• 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 2

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.

Biomass

Population

Kernel Number

Corn Field

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)

Example 3

60

80

100

120

46

810

120

200

400

600

800

Biomass

Validation Data

Plant Population

Ke

rne

l nu

mb

er

* Actual KN

Predicted KN

Example 3

200 300 400 500 600 700200

250

300

350

400

450

500

550

600

650

700Predicted KN vs. Actual KNP

red

icte

d K

N

Actual KN

KNPred=30.651+0.9321*KNActual

* Validation data

o Training data

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

Example 3

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.

• 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

Weaknesses

If you want to know more about Neural Network,

• http://www.doc.ic.ac.uk/~nd/surprise_96/journal/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)

1940 1960 19691970 1980 1990 2000

Research & Interest of Neural Network

Time

?“Neural networks do not perform

miracles. But if used sensibly they

can produce some amazing

results.”

-- C. Stergiou and D. Siganos