The document discusses the application of artificial intelligence (AI) and deep learning in bioinformatics, specifically focusing on areas such as genomics, transcriptomics, and metagenomics. It outlines various AI challenges, models, and methods used for tasks like sentiment analysis, taxonomic classification, and phenotype classification. The document also provides insights into how to start implementing AI in bioinformatics and emphasizes the importance of understanding general AI principles before delving into specific problems.

1. Ali Kishk
Research Assistant @ Nile University
in Bioinformatics

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5. Outline
● AI (Types, Development)
● Deep Learning (Architecture)
● Bioinformatics Fields
● Input formats for AI
● AI Challenges in Biology
● Example: (Proteomics, Transcriptomics)
● Metagenomics: @ NU
● Taxonomic Classification
● Phenotype Classification
● How to begin in AI in Bioinformatics

6. AI
Applications
7

7. Why Artificial Intelligence?

8. Artificial Intelligence Classification
AI
Supervised Un-Supervised
RegressionClassification
Reinforcement
Learning

9. Supervised
Classification Regression

10. Un-Supervised
Reinforcement
Learning

11. Artificial Intelligence: Development
AI
Deep
Learning
Classical
Machine
Learning

12. Bioinformatics Fields
Genomics
Transcriptomics
Proteomics

13. Input formats for AI
ACTCTCTCTGCTACTCGCA
Sequence
ACTCTCTCTGCTACTCGCA
Sequence
ACTCTCTCTGCTACTCGCRA
Image
GC%,
Kmer frequency,
TFIDF
Features

14. Deep Learning Architectures
Multi-layer
perceptron
Convolutional
neural network
Recurrent neural
network
Complexity Low Medium High
Examples ResNet LSTM, GRU
Main Applications Tabular data Computer Vision Sequence classification
Machine Translation
15

15. AI Challenges in Biology
● High # features
● Low # samples
● High # classes
● Feature sparsity
● Class imbalance

16. AI Challenges in Biology
Genomics Transcriptomics Metagenomics
Unique analysis step Variant calling
Differential expressed
gene analysis
Taxonomic
assignment
Analysis output
Variant calling file of
SNPs or CNVs
Differential expressed
genes (DEGs)
Taxonomy table /
OTU table
Features Sparsity Very sparse Dense Sparse
Number of Features 1M to 10M 10K : 50K
OTU table: 100s to
1K
Kmer content: 4 ^
kmer size

17. AI in Proteomics
Subcellular Localization
Input:
Molecular Weight, Polarity ..
Output:
Location ( Nucleus, Cytoplasm,
Extracellular. membrane)
Format:
Protein Sequence Features
Wei, Leyi, et al. "Prediction of human protein subcellular localization using deep learning." Journal of Parallel and Distributed Computing 117 (2018):
212-217.

18. AI in Transcriptomics
Differential Expression Prediction
Input:
1000 gene expression
Output:
20,000 gene expression
Format:
Features (Differential Expression)
Subramanian, Aravind, et al. "A next generation connectivity map: L1000 platform and the first 1,000,000
profiles." Cell 171.6 (2017): 1437-1452.

19. AI in Metagenomics
1- AmpliconNet: Taxonomic Assignment using Deep Learning (Published)
2- Phenotype Classification using Machine Learning (Published)
3- Metafy: Phenotype Classification using Deep Learning (Not published)

20. General 16S rRNA analysis steps
1- Quality Control
2- Merging
3- Trimming & Filtration
4- Taxonomic Assignment
5- Statistical analysis
21

21. AI in Metagenomics
Taxonomic Assignment
Input: 16s rRNA gene sequence
Output: Phylum, Class, Order. Family, Genus
Format: Sequence itself
Kishk, Ali, and Mohamed El-Hadidi. "AmpliconNet: Sequence Based Multi-layer Perceptron for Amplicon Read Classification Using Real-time
Data Augmentation." 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018.

22. Taxonomic assignment programs
Alignment Based Prediction-Based / ML-Based
Pros Highly accurate Faster in prediction
No reference database needed
Cons Computational expensive,
increasing if applied for sub-species
level
Less accurate
Most tools use k-mer frequency
Example BLAST RDP, 16S classifier
23

23. Training data in ML-based
1- Full 16s rRNA gene
2- High Variable Region Specific
Image source: https://www.lcsciences.com/discovery/applications/genomics/16s-rrna-gene-sequencing-landing/16s-gene/ 24

24. AmpliconNet Goal
1- Modeling the direct sequence rather than sequence features.
2- Reduce taxonomic classification time from weeks to hours
using DL (Over GPU and TPU)
3- Using simple neural network (Trainable model on average PCs)
25

25. - HVR Specific model
- Direct sequence modeling
- Manual search of many DL architectures
- Data augmentation
AmpliconNet Approach

26. Sequence Input Example
“I like this movie.”
“I don’t like this movie.”
27
Sorted Corpus
1. Don’t
2. Like
3. I
4. This
5. Movie
One Hot Encoding
3 2 4 5 >>> HAPPY
3 1 2 4 5 >>> SAD
Vocab_size
Zero Padding
3 2 4 5 0 >>> HAPPY
3 1 2 4 5 >>> SAD

27. DNA Words !

28. AmpliconNet Vs RDP
RDP* AmpliconNet
Input Type Sequence features Sequence itself
Input Kmer frequency Sequence of kmers
Model Naive Bayes Multilayer Perceptron
16S rRNA gene
training
The whole gene (one
model)
HVR Specific (multiple
models)
Advantage Faster in training Preserve kmer position
● Wang, Qiong, et al. "Naive Bayesian classifier for rapid assignment of rRNA sequences into the new
bacterial taxonomy." Appl. Environ. Microbiol. 73.16 (2007): 5261-5267.

29. AmpliconNet Vs RDP F1_Score on V2 HVR
F1_Score for AmpliconNet & RDP on the simulated test dataset
Phylum Order Class Family Genus
# Classes 28 50 162 348 1853
AmpliconNet 99.85% 99.80% 99.49% 99.14% 95.55%
RDP 99.86% 99.81% 99.50% 99.13% 89.79%
30

30. AmpliconNet paper
Publication: in BIBM 2018 Conference Paper

31. AmpliconNet Code:
Github: https://github.com/ali-kishk/AmpliconNet

32. 2- Phenotype Classification
using ML:
Goal:
Using a classifier on the raw data, to
avoid alignment time.
Techniques:
Kmer Frequency
SVM, Random Forest
Results:
78% test F1, on CRC data (277 sample)

33. 2- Phenotype Classification of Colon Cancer
Publication: in CIBEC 2018 Conference Paper

34. 3- Phenotype Classification using Deep Learning:
Goal:
Reduce the number of features by deep learning
Results:
MicroPheno * Metafy (our approach)
Input Kmer frequency Kmer frequency
Model SVM / RF/ MLP DL feature extraction + SVM
Test F1 on CRC 84 % 89%
* Asgari, Ehsaneddin, et al. "MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene
sequencing using a k-mer based representation of shallow sub-samples." Bioinformatics 34.13 (2018): i32-i42.

35. How to begin in AI in Bioinformatics:
1- Choose an OMICs field (genomics, transcriptomics,..)
2- Choose unsolved problem / need optimization
3- Choose a NGS platform
4- Search for recent models / architectures in this problem
5- Keep yourself updated (Google Scholar Alert)
6- Define the problem challenges ( class imbalance)
7- Search for recent models / architectures for these challenges.

36. Conclusion
Strength yourself in AI first in general problems (NLP, Vision)
Avoid the AI hype

37. Thank You
Contact:
ph.ali.kish@gmail.com