STATE UNIVERSITY OF NEW YORK AT OSWEGO

MASTERS THESIS

Biomedical and Health Informatics

Analysis of Epigenetics and Epidemiology of Acute Myeloid Leukemia with

Machine Learning

Author: Supervisor:

Sarah Mason Isabelle Bichindaritz

Committee:

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TABLE OF CONTENTS

INTRODUCTION ............................................................................................................................ 1

SCOPE ........................................................................................................................................... 1

AIMS ............................................................................................................................................. 2

RELATED WORK ............................................................................................................................ 3

MATERIALS AND METHODS .......................................................................................................... 4

PREPROCESSING METHODS ............................................................................................................ 4 PROCESSING METHODS ................................................................................................................... 4

RANDOM FOREST ........................................................................................................................ 5 MULTILAYER PERCEPTRON ......................................................................................................... 5 SUPPORT VECTOR MACHINE ....................................................................................................... 5

DATASETS ..................................................................................................................................... 6

RESULTS ........................................................................................................................................ 7

RANDOM SET ................................................................................................................................... 7 RANDOM FOREST ........................................................................................................................ 8 MULTILAYER PERCEPTRON ......................................................................................................... 8 SEQUENTIAL MINIMAL OPTIMIZATION ...................................................................................... 9

TRAINING SET .................................................................................................................................. 9 RANDOM FOREST ........................................................................................................................ 9 MULTILAYER PERCEPTRON ....................................................................................................... 10 SEQUENTIAL MINIMAL OPTIMIZATION .................................................................................... 11

LOOCV SET ..................................................................................................................................... 12 RANDOM FOREST ...................................................................................................................... 13 MULTILAYER PERCEPTRON ....................................................................................................... 14 SEQUENTIAL MINIMAL OPTIMIZATION .................................................................................... 15

DISCUSSION ................................................................................................................................ 16

CONCLUSION .............................................................................................................................. 18

BIBLIOGRAPHY ............................................................................................................................ 19

TABLE OF TABLES ........................................................................................................................ 21

ACKNOWLEDGEMENTS ............................................................................................................... 21

ABSTRACT

Epidemiology of Acute Myeloid Leukemia shows strong genetic and

epigenetic links by types and severity. To study the disease, patient samples

are translated into data. Using advanced data analytics techniques,

supervised machine learning, epigenetic research acquires efficiency for

synthesis and building knowledge based on clinical data. There are known

factors supported by research. The combination of factor produces higher

severity in Acute Myeloid Leukemia by clinical considerations, AML subclass,

and methylation.

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INTRODUCTION

Cancer is one of the leading causes of death and impairment in the United States.

As of 2018, an estimated 1,735,350 new cases of cancer will be diagnosed in the

United States and 609,640 people will die from the disease (www.cancer.gov).

Approximately 38.4% of men and women will be diagnosed with cancer at some

point during their lifetimes (based on 2013–2015 data). Estimated national

expenditures for cancer care in the United States in 2017 were $147.3 billion. In

future years, costs are likely to increase as the population ages and

cancer prevalence increases. Costs are also likely to increase as new, and often

more expensive; treatments are adopted as standards of care.

Understanding the progression of cancer is an important factor in developing

treatment plans to prevent or reverse progression. Acute Myeloid Leukemia is a

blood disorder that is a cancer of the blood cells. Without treatment, it becomes

severe and often fatal. This is a disease where platelet, red, or white blood cells can

be abnormal.

To study the biochemical process and features of Acute Myeloid Leukemia, we

can use preexisting clinical and methylation data to apply techniques to compare

based on patient statistics to form a conclusion of tools and application of

knowledge of Acute Myeloid Leukemia. The costs of cancer are high, financial, and

personal. Finding better ways to treat and manage to reduce impact on people and

society requires better knowledge of how cancer develops.

SCOPE

Creating a plan to analyze cancer epigenetics to assist in academic understanding

the nature of cancer, is significant if repeatable. This can create a guide of how to

determine the path of tumor progression for accurate prognosis and treatments for

a positive outcome.

Two tumors with the same genetics can have different progressions. This makes

research difficult to provide therapies and complicates decisions regarding

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treatment for patients. Epigenetics includes methylation of the DNA ladder. This

pattern is a difference in cells and influences gene expression. Current thought in

oncology is that the changes in methylation of DNA impact the behavior of tumors

causing one tumor to be more aggressive and another tumor to be a more positive

prognosis (Angermueller, Lee, Reik, and Stegle 2017). There are different subtypes

of epigenetics. Methylation, which is the quantity of methyl molecules attached to

the ladder of DNA, can change and vary between individuals and over time.

Methylation can change and influence the action of genetic expression. This is a

current area of cancer research. Is there correlation between methylation, type, and

survival of AML.

Epigenetics can change over time due to many factors such as lifestyle and

environment. It reflects the effect of change due to factors by comparing recurrent

data samples from the same patient and large sets of similar patients. This allows

synthesis of connections between factors yielding wisdom about cancer

progression. There are several known regions associated with Acute Myeloid

Leukemia and methylation, three main regions are selected for analysis of

methylation by differentiation of AML.

Genetic Epidemiology has changed over time. “The concept of ‘disease’ used in

epidemiology is no longer limited to infectious diseases (Frérot, M., Lefebvre, A.,

Aho, S., Callier, P., Astruc, K., & Aho Glélé, L. S. 2018).” Epidemiology is regarded as

the study of health in a population. Epigenetic Epidemiology is the same with

epigenetics. The analytics of the methylation data shows validity for the field.

Studying epigenetics typically involves large datasets. Machine Learning and

Analytics Tools like Weka are targeting to this type of application, working with large

and dense datasets.

AIMS

Results of this research are to define and analyze methylation comparing

different methods. The outcomes and prognosis in relationship to methylation and

gender within the sample set show tool implementation and health data. This will

yield a positive or negative hypothesis that methylation contributes to the fatality

of AML and will give insight to relationship with subtype of this blood disorder. Not

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only is this a study of epigenetic epidemiology, the work will show effectiveness of

Machine Learning in Weka for the tasks involved. Finally, this will provide data-based

guidance for health screenings based on prediction of health based on features.

RELATED WORK

Previous works include predicting genetic and epigenetic structure based on

machine learning (C. Angermueller, H. Lee, W. Reik, and O. Stegle 2017), correlation

of methylation patterns and cancer (Jelinic, P. and Shaw, P. 2007). Neither paper is

specific to AML and cancer. However, they show link between cancer progression

and methylation.

Looking at the study of Leukemias, academic research is prolific. There is one very

similar article: “DNA Methylation Events as Markers for Diagnosis and Management

of Acute Myeloid Leukemia and Myelodysplastic Syndrome,” by Geórgia Muccillo

Dexheimer, Jayse Alves, Laura Reckziegel, Gabrielle Lazzaretti, and Ana Lucia

Abujamra. However, there are different articles basing their work on this publication

by refining and focusing on different regions of genetic data. I will focus on the

quantity of methylation, variation of recurrence, mortality and survival by age and

gender. This will be a study to show the relationships and influencers that change

the outcome of the mutations involved in Acute Myeloid Leukemia.

Developing a proper study with people is time intensive and costly. By using

available data, we can eliminate bioethics concerns over live participants with less

time to create and maintain an experiment. This also means that There is high value

to these methods and translational research using pre-existing data via

computational analytics.

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MATERIALS AND METHODS

PREPROCESSING METHODS

In R, using code, the data from the clinical is merged with methylation data to

create a large dataset. By selecting the rows to include within the merge, only the

data required for this project is retained. Empty entries are removed. This produces

a dataset that is useful and manageable.

Preprocessing in Weka is a multi-step process. First, the arff viewer is used to

import the comma separated value file and convert to Attribute-Relation File

Format. After importing the file from WEKA Explorer, selecting preprocessing filters

for data, and reviewing the plot matrix to validate the files, the next step is

processing data.

PROCESSING METHODS

Weka is used in this study of the LAML Datasets from FireBrowse. Within Weka,

there are many selections in multiple menus. These are all mathematically based on

classification and statistics techniques for data analysis. All classifiers used are

Machine Learning tools. There are three types of classifiers representing three

divisions of machine learning, statistical, tree, and neural network. These are

Machine Learning techniques because they are coded supervised learning by Weka

to resolve large scale data to a true or false hypothesis. Due to scale of the project,

training set, LOOCV was used in the menu for classification.

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RANDOM FOREST

Random forest is a method for classification that creates a multitude

of decision trees at training time and finds the mode of the classes

(classification) of the individual trees. Method used in Weka is

RandomForest. For the research, only classification was included for

results and analysis.

MULTILAYER PERCEPTRON

In Weka, MultiLayer Perceptron is a variant of Long short-term

memory (LSTM) an artificial recurrent neural network (RNN) method that

is supervised machine learning. Unlike standard feedforward neural

networks, LSTM has feedback connections. It can not only process single

data points (such as images), but also entire sequences of data (such as

speech or video).

LSTM networks are well-suited to classifying, processing and making

decisions based on time series data, since there can be lags of unknown

duration between important events in a time series. LSTMs were

developed to deal with the exploding and vanishing gradient

problems that can be encountered when training traditional RNNs.

Relative insensitivity to gap length is an advantage of LSTM over

RNNs, hidden Markov models and other sequence learning methods in

numerous applications (Wikipedia).

SUPPORT VECTOR MACHINE

Support-vector machines are supervised learning models with

associated learning algorithms that analyze data used

for classification and regression analysis. Given a set of training examples,

each marked as belonging to one or the other of two categories, an SVM

training algorithm builds a model that assigns new examples to one

category or the other with classification occurring due mapping above or

below a linear boundary set within the model. An SVM model is a

representation of the examples as points in space, mapped so that the

examples of the separate categories are divided by a clear gap that is as

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wide as possible. New examples are then mapped into that same space

and predicted to belong to a category based on the side of the gap on

which they fall. SMO was used in Weka for support-vector machine

analysis. SMO is usually seen as a preprocessing for SVM in Weka. SMO is

adequate for small dataset processing for SVM analysis.

DATASETS

Dataset including data and metadata for epigenetics, genetics, and clinical

information was obtained from Firebrowser. The dataset used is a union of the

LAML Preprocess data and Clinical file under the methylation data tab. This

includes more than needed for the analysis, so set used was simplified to have

necessary data only the smaller set was run with normalization and classification

in WEKA.

In computational terms, the sets are made of attributes, or factors, and

instances. Attributes are columns of the tables. The instances in computational

analytics are rows in each table. Files were manipulated from comma separated

value files to the .arff file that is associated with WEKA via the .arff viewer, which

is a conversion tool.

DATA SOURCES

SAMPLES FACTORS SIZE

PREPROCESS.CSV 195 38557 127.79MB

CLINICAL_CLIN.CSV 201 167 222KB

Table 1: Data File Description

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DATA DESCRIPTION FOR ANALYSIS

Label Quantity Description

Samples 192 Unique Samples

Count of Genes 3 TET2, TPMT,

DNMT3A

Clinical Factors 1 Morphology

Morphology

Values

8 m0, m1, m2, m3, m4,

m5, m6, m7

Table 2: Subset Data Description

RESULTS

Three different types of supervised learning were used to classify the dataset:

Random Forest (RF), MultiLayer Perceptron (MLP), and Sequential Minimal

Optimization (SMO). Each type was run three times in Weka to show accuracy of the

algorithms. Each run of the three types were the same output. There is variation in

results from processing between the types of classification and modeling. Training set

and leave one out cross validation, LOOCV, options are used in modeling with a split

training set option for a set of random data.

RANDOM SET

Random dataset was created by selecting a portion of the dataset and

performing preprocessing functions. The accuracy is high where an acceptable

result is better than 1/8th of the set or 12.5%. This is based on the eight morphology

values for classification. The random datasets were each created by sampling the

set by using a 90% training 10% testing.

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Random Forest

RANDOM FOREST CLASSIFIER STATISTICS FOR RANDOM DATA

CORRECTLY CLASSIFIED INSTANCES 15.7895%

INCORRECTLY CLASSIFIED INSTANCES 84.2105%

KAPPA STATISTIC 0.0162

MEAN ABSOLUTE ERROR 0.2152

ROOT MEAN SQUARED ERROR 0.3384

RELATIVE ABSOLUTE ERROR 100.6146%

ROOT RELATIVE SQUARED ERROR 101.4547% F-MEASURE N/A

AUC .557

TOTAL NUMBER OF INSTANCES 192

Table 3: Random Forest Random Data Statistics

MultiLayer Perceptron

MULTILAYER PERCEPTRON CLASSIFIER STATISTICS FOR RANDOM DATA

CORRECTLY CLASSIFIED INSTANCES 21.0526% INCORRECTLY CLASSIFIED INSTANCES 78.9474% KAPPA STATISTIC 0.0436 MEAN ABSOLUTE ERROR 0.2152 ROOT MEAN SQUARED ERROR 0.4095 RELATIVE ABSOLUTE ERROR 100.6049% ROOT RELATIVE SQUARED ERROR 122.7941% F-MEASURE .211 AUC N/A TOTAL NUMBER OF INSTANCES 192

Table 4: MultiLayer Perceptron Random Data Statistics

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Sequential Minimal Optimization

SEQUENTIAL MINIMAL OPTIMIZATION STATISTICS FOR RANDOM DATA

CORRECTLY CLASSIFIED INSTANCES 10.5263 % INCORRECTLY CLASSIFIED INSTANCES 89.4737 % KAPPA STATISTIC -0.0419 MEAN ABSOLUTE ERROR 0.2119 ROOT MEAN SQUARED ERROR 0.3305 RELATIVE ABSOLUTE ERROR 99.0956 % ROOT RELATIVE SQUARED ERROR 99.0962 % F-MEASURE N/A AUC 0.548 TOTAL NUMBER OF INSTANCES 192 Table 5: Sequential Minimal Optimization Random Data Statistics

TRAINING SET

The training set method is using the dataset to train and to test. All computation

is done twice, once to train and once to test. This method allows for a split where

a portion of the data is assigned to training and the rest to test. For the analytics,

the whole dataset was used for both training and testing with morphology as the

classifier defined with eight values corresponding to subtypes of Acute Myeloid

Leukemia.

Random Forest

RANDOM FOREST CLASSIFIER STATISTICS

CORRECTLY CLASSIFIED INSTANCES

100%

INCORRECTLY CLASSIFIED INSTANCES

0%

KAPPA STATISTIC 1 MEAN ABSOLUTE ERROR 0.0752 ROOT MEAN SQUARED ERROR 0.1195 RELATIVE ABSOLUTE ERROR 36.4421% ROOT RELATIVE SQUARED ERROR 37.2455% F-MEASURE 1 AUC 1 TOTAL NUMBER OF INSTANCES 192

Table 6: Random Forest Classifier Statistics

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Random Forest Classifier Confusion Matrix A B C D E F G H 41 0 0 0 0 0 0 0 A=m4 0 19 0 0 0 0 0 0 B=m3 0 0 19 0 0 0 0 0 C=m0 0 0 0 41 0 0 0 0 D=m1 0 0 0 0 44 0 0 0 E=m2 0 0 0 0 0 22 0 0 F=m5 0 0 0 0 0 0 3 0 G=m6 0 0 0 0 0 0 0 3 H=m7 Table 7: Random Forest Confusion Matrix

From analysis, the subtype m2 is most common and severe. The type m2

corresponds to myeloblastic leukemia with maturation using the SEER AML

diagram available from NIH. The results from Random Forrest are highly accurate

and have a low error rate as shown in the AUC value. Both training and testing are

done on whole dataset used in analysis.

MultiLayer Perceptron

MULTILAYER PERCEPTRON CLASSIFIER STATISTICS

CORRECTLY CLASSIFIED INSTANCES 54.1667% INCORRECTLY CLASSIFIED INSTANCES 45.8333% KAPPA STATISTIC 0.4273 MEAN ABSOLUTE ERROR 0.1584 ROOT MEAN SQUARED ERROR 0.276 RELATIVE ABSOLUTE ERROR 76.7694% ROOT RELATIVE SQUARED ERROR 86.0416% F-MEASURE N/A AUC .79 TOTAL NUMBER OF INSTANCES 192

Table 8: MultiLayer Perceptron Statistics

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MultiLayer Perceptron Classifier Confusion Matrix A B C D E F G H 28 0 0 0 0 0 0 0 A=m4 2 0 1 3 12 1 0 0 B=m3 2 0 9 3 4 1 0 0 C=m0 2 0 1 33 5 0 0 0 D=m1 3 0 6 4 31 0 0 0 E=m2 2 0 4 8 5 3 0 0 F=m5 0 0 0 1 1 1 0 0 G=m6 0 0 0 0 1 2 0 0 H=m7

Table 9: MultiLayer Perceptron Confusion Matrix

Acute Myeloid Leukemia has a type defined by morphology. The results from

MLP show that subtype m1 is most common. The disease subtype is more common

in men. The accuracy is low but acceptable, making this type of process not best fit

for the data. The result does not need to fit an expected result, but there are errors

when classifying for morphology, or subtype, of AML. The AUC is acceptable and

higher than the value of Random Forest classification with a random dataset. In

this result, m1 and m2 are significantly higher and well classified. The MLP scheme

default is for two hidden layers and .01 ridge.

Sequential Minimal Optimization

SEQUENTIAL MINIMAL OPTIMIZATION CLASSIFIER STATISTICS

CORRECTLY CLASSIFIED INSTANCES 99.4792% INCORRECTLY CLASSIFIED INSTANCES 0.5208% KAPPA STATISTIC 0.9937 MEAN ABSOLUTE ERROR 0.1875 ROOT MEAN SQUARED ERROR 0.2913 RELATIVE ABSOLUTE ERROR 90.9144% ROOT RELATIVE SQUARED ERROR 90.8069% F-MEASURE .995 AUC .999 TOTAL NUMBER OF INSTANCES 192

Table 10: Sequential Minimal Optimization Statistics

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Sequential Minimal Optimization Classifier Confusion Matrix

A B C D E F G H 41 0 0 0 0 0 0 0 A=m4 0 19 0 0 0 0 0 0 B=m3 0 0 18 0 1 0 0 0 C=m0 0 0 0 41 0 0 0 0 D=m1 0 0 0 0 44 0 0 0 E=m2 0 0 0 0 0 22 0 0 F=m5 0 0 0 0 0 0 3 0 G=m6 0 0 0 0 0 0 0 3 H=m7

Table 11: Sequential Minimal Optimization Confusion Matrix

The results from SMO show that subtype m2 is most common. The accuracy is

good with 99.4792% accuracy and .999 AUC and high percent expected

classification. This classifier shows high occurrence of subtypes m1 and m4 in

addition to m2. This corresponds to three different types of myeloblastic leukemia:

combination myeloblastic monoblastic leukemia and myeloblastic leukemia with

and without maturation.

LOOCV SET

LOOCV is an acronym for Leave One Out Cross Validation. This model takes the

dataset and divides the set into two portions. The larger portion is used for training

and testing, as is the smaller set. The two results are then compared to validate.

For models, the total number of samples, 192, is used minus one to leave one out.

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Random Forest

RANDOM FOREST CLASSIFIER STATISTICS

CORRECTLY CLASSIFIED INSTANCES

19.27%

INCORRECTLY CLASSIFIED INSTANCES

80.72%

KAPPA STATISTIC -0.03 MEAN ABSOLUTE ERROR 0.21 ROOT MEAN SQUARED ERROR 0.32 RELATIVE ABSOLUTE ERROR 99.06% ROOT RELATIVE SQUARED ERROR 100.27% F-MEASURE N/A AUC .475 TOTAL NUMBER OF INSTANCES 192

Table 12: Random Forest Classifier Statistics

Random Forest Classifier Confusion Matrix A B C D E F G H 6 1 1 8 25 0 0 0 A=m4 2 0 0 0 17 0 0 0 B=m3 5 0 0 5 7 2 0 0 C=m0 4 0 0 14 19 3 1 0 D=m1 10 0 2 13 17 2 0 0 E=m2 4 0 1 6 11 0 0 0 F=m5 0 0 0 1 2 0 0 0 G=m6 0 0 0 0 3 0 0 0 H=m7 Table 13: Random Forest Confusion Matrix

From analysis, we know that there is a spread of results. The accuracy level is

low compared to the same analysis using a model based on the training set and

lower than random. There is more misclassified that correct. The most common

subtype is m2.

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MultiLayer Perceptron

MULTILAYER PERCEPTRON CLASSIFIER STATISTICS

CORRECTLY CLASSIFIED INSTANCES 23.43% INCORRECTLY CLASSIFIED INSTANCES 76.56% KAPPA STATISTIC 0.05 MEAN ABSOLUTE ERROR 0.20 ROOT MEAN SQUARED ERROR 0.39 RELATIVE ABSOLUTE ERROR 94.54% ROOT RELATIVE SQUARED ERROR 122.45% F-MEASURE N/A AUC .527 TOTAL NUMBER OF INSTANCES 192

Table 14: MultiLayer Perceptron Statistics

MultiLayer Perceptron Classifier Confusion Matrix A B C D E F G H 9 2 5 11 11 3 0 0 A=m4 4 4 0 3 6 2 0 0 B=m3 5 1 2 6 4 1 0 0 C=m0 10 1 1 17 6 6 0 0 D=m1 8 8 3 9 12 4 0 0 E=m2 6 2 2 7 4 1 0 0 F=m5 1 0 0 1 1 0 0 0 G=m6 1 0 0 0 2 0 0 0 H=m7 Table 15: MultiLayer Perceptron Confusion Matrix

Acute Myeloid Leukemia has a type defined by morphology. The results from

MLP show that subtype m1 and m2 are the most common predictions. The

accuracy is lower than with the training set, however much higher than the

results for MultiLayer Perceptron Random Set. However, using the LOOCV

method seems to provide higher effectiveness than training testing set-up for

classification by morphology, which defines leukemia subtype. The results are

obtained from a 191 Training and 1 Testing set-up. The MLP scheme default is for

two hidden layers and .01 ridge.

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Sequential Minimal Optimization

SEQUENTIAL MINIMAL OPTIMIZATION CLASSIFIER STATISTICS

CORRECTLY CLASSIFIED INSTANCES 21.35% INCORRECTLY CLASSIFIED INSTANCES 78.65% KAPPA STATISTIC 0.01 MEAN ABSOLUTE ERROR 0.21 ROOT MEAN SQUARED ERROR 0.32 RELATIVE ABSOLUTE ERROR 99.95% ROOT RELATIVE SQUARED ERROR 100.28 F-MEASURE .995 AUC .999 TOTAL NUMBER OF INSTANCES 192

Table 16: Sequential Minimal Optimization Statistics

Sequential Minimal Optimization Classifier Confusion Matrix A B C D E F G H 11 0 3 9 17 1 0 0 A=m4 5 1 0 4 8 1 0 0 B=m3 5 1 2 4 5 2 0 0 C=m0 8 0 0 14 16 3 0 0 D=m1 12 5 1 12 12 2 0 0 E=m2 4 1 1 7 8 1 0 0 F=m5 1 0 0 1 1 0 0 0 G=m6 1 0 0 0 2 0 0 3 H=m7 Table 17: Sequential Minimal Optimization Confusion Matrix

The subtype m1 is more commonly predicted, myeloblastic leukemia without

maturation. The AUC value is high showing either excellent results or an overfit to

the data requiring further evaluation. The data shows there is severity compared

to training and test set data. The performance results are lower, which indicate a

harder experimental set-up and test framework.

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DISCUSSION

The morphology codes m1 and m2 are repeated in prediction results. This

corresponds to Myeloblastic Leukemia with and without maturation. This can be

seen in the results of Random Forest, MultiLayer Perceptron, and Sequential

Minimal Optimization. Due to small datasets using training set is more accurate

with high classification and AUC values.

ACUTE MYELOID LEUKEMIA MORPHOLOGY TABLE

Classification Description

m0 Undifferentiated leukemia Stem cells predominate or cell type Unidentified

m1 Myeloblastic leukemia without maturation

Immature white blood cells predominate

m2 Myeloblastic leukemia with maturation

Partial differentiation

Promyelocytic leukemia m3a without eosinophilia m3b with eosinophilia (98603)

Promyelocytes predominate

Combination myeloblastic-monoblastic leukemia m4 acute myelomonocytic leukemia m4e0 acute myelomonocytic leukemia with eosinophilia

Each component constitutes greater than 20% of the blasts in the bone Marrow

m5a acute monocytic leukemia without differentiation m5b acute monocytic leukemia with differentiation (promonocytic)

m6 Erythroleukemia Immature red and white cells predominate

m7 Megakayrocytic leukemia Monoblasts predominate

Table 18: Acute Myeloid Leukemia Morphology

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Classifying into eight classes, the main subclasses of AML, with a small number

of samples is a very difficult task, which explains why many accuracy results are

below 50%. The classes are m0, m1, m2, m3, m4, m5, m6, m7 with the a, b, and e

subtypes not differentiated in the dataset. However, the results using eight classes

is above the random classification rate of 12.5% in most analysis runs. Therefore,

they provide useful predictions. Two morphology subtypes, m1 and m2, are more

correlated with the methylation profiles. From the table, this is a higher incidence

of myeloblastic leukemia with or without maturation.

Methylation appears correlated for occurrence and severity of Acute Myeloid

Leukemia by comparing predictions of status, years lived after diagnosis, and

methylation. Recent literature in oncology states known gene and methylation

groups that play a role in causing malignancy to develop into Acute Myeloid

Leukemia. Changes in the pattern compared to random sets shows higher

methylation in regions yields strong connection to patients with AML, and this also

shows links to subtypes.

Weka is a good choice for epigenetic and genetic computing. It is graphical

interface for Machine Learning with good capability for big data. There are

description of offerings and menus in Weka to help assist in picking proper

functions such as picking preprocessing filters based on data type.

The three classifiers used are supervised machine learning. Using Weka, the

algorithm can be modified by using terms to fit, such as “-m” with SMO to yield

AUC (Area Under the Curve). Classification in small sets is often difficult with

classification due to not enough variance to select a pattern. Using the approaches

with small set clinical and methylation data, high accuracy can be obtained with

checking required to sort misclassification.

Full dataset of whole genome methylation was not used. Only a portion

containing data related to three known leukemic regions due to resource

limitations for processing. Interestingly, errors occur when processing goes to

failure due to hardware issues and settings that cannot be changed due to my

operating system.

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CONCLUSION

The methylation pattern is different between subtypes of AML within known

leukemic regions. Myeloblastic Leukemia is aggressive and more common with

more methylation and myeloblastic leukemias are strongly correlated with

methylation patterns. When considering tools for analytics in big data genetics,

Weka provides more native tools for quantitative research and the graphical

interface with panels is more organized with heavy resource requirements. An area

of further research is setting up comparisons of binary classification and classifying

on a larger quantity of features.

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TABLE OF TABLES

TABLE 1: DATA FILE DESCRIPTION............................................................................................... 6 TABLE 2: SUBSET DATA DESCRIPTION ......................................................................................... 7 TABLE 3: RANDOM FOREST RANDOM DATA STATISTICS ............................................................ 8 TABLE 4: MULTILAYER PERCEPTRON RANDOM DATA STATISTICS .............................................. 8 TABLE 5: SEQUENTIAL MINIMAL OPTIMIZATION RANDOM DATA STATISTICS ........................... 9 TABLE 6: RANDOM FOREST CLASSIFIER STATISTICS .................................................................... 9 TABLE 7: RANDOM FOREST CONFUSION MATRIX .................................................................... 10 TABLE 8: MULTILAYER PERCEPTRON STATISTICS ...................................................................... 10 TABLE 9: MULTILAYER PERCEPTRON CONFUSION MATRIX ...................................................... 11 TABLE 10: SEQUENTIAL MINIMAL OPTIMIZATION STATISTICS ................................................. 11 TABLE 11: SEQUENTIAL MINIMAL OPTIMIZATION CONFUSION MATRIX ................................. 12 TABLE 12: RANDOM FOREST CLASSIFIER STATISTICS ................................................................ 13 TABLE 13: RANDOM FOREST CONFUSION MATRIX .................................................................. 13 TABLE 14: MULTILAYER PERCEPTRON STATISTICS .................................................................... 14 TABLE 15: MULTILAYER PERCEPTRON CONFUSION MATRIX .................................................... 14 TABLE 16: SEQUENTIAL MINIMAL OPTIMIZATION STATISTICS ................................................. 15 TABLE 17: SEQUENTIAL MINIMAL OPTIMIZATION CONFUSION MATRIX ................................. 15 TABLE 18: ACUTE MYELOID LEUKEMIA MORPHOLOGY ............................................................ 16

ACKNOWLEDGEMENTS

In the design, and the process of this project, I learned much more than

about using a prompt. Thank you to Dr Bichindaritz and SUNY Oswego for their

support and guidance in achieving this milestone.