ARTIFICIAL INTELLIGENCE FOR BIOLOGICAL SCIENCES ML algorithms to find associations across biological data

1. ARTIFICIAL INTELLIGENCE
FOR BIOLOGICAL SCIENCES
ML ALGORITHMS TO FIND ASSOCIATIONS
ACROSS BIOLOGICAL DATA
PREPARED BY:
MS. ABIRAMI.S
M. SC., MICROBIOLOGY
UNDER THE GUIDANCE OF :
MRS. S. SELVAJEYANTHI
ASST PROFESSOR
DEPARTMENT OF MICROBIOLOGY,
SNMV CAS

2. CONTENT
• Machine Learning
• Types of ML
• ML algorithm
• ML algorithm to find association across biological
data
• Conclusion

3. MACHINE LEARNING
• ML stands for "machine learning.“
• It's a subset of artificial intelligence that focuses on the development of
algorithms and models that allow computers to learn from data and
improve their performance on specific tasks over time.
• Instead of being explicitly programmed, these algorithms use data to
identify patterns, make predictions, and make decisions.
• Machine learning is used in various applications, such as image recognition,
language processing, recommendation systems, and more.

4. TYPES OF ML
• Supervised Learning: Algorithms learn from labeled data,
where the input data is paired with the corresponding correct
output. Common algorithms include linear regression, decision
trees, and support vector machines.
• Unsupervised Learning: Algorithms work with unlabeled data
to discover patterns and relationships within the data. Examples
include clustering algorithms like k-means and dimensionality
reduction techniques like principal component analysis (PCA).
• Reinforcement Learning: Algorithms learn through trial and
error, receiving feedback in the form of rewards or penalties
based on their actions. This approach is often used in training
agents to perform specific tasks, like playing games or
controlling robots.

5. ML ALGORITHM
• A machine learning (ML) algorithm is a set of rules and mathematical
procedures that enables a computer to learn patterns and make
predictions or decisions based on data, without being explicitly
programmed.
• ML algorithms are designed to improve their performance over time as
they process more data, allowing them to adapt and generalize from
examples.
• ML algorithms are a fundamental part of the field of artificial intelligence
(AI), and their applications range from image and speech recognition to
recommendation systems, autonomous vehicles, and more.
• These algorithms can be categorized into different types

6. ML ALGORITHMS TO FIND ASSOCIATION ACROSS BIOLOGICAL
DATA
Machine learning algorithms play a crucial role in analyzing and identifying
associations across biological data. Here are some commonly used ML
algorithms for this purpose:
• Apriori Algorithm: Widely used in association rule mining, the Apriori
algorithm identifies frequent itemsets in datasets. In biology, it can
discover co-occurrence patterns among genes, proteins, or metabolites,
revealing potential interactions or functional relationships. In genomics, for
example, Apriori can reveal associations between mutations in different
genes that occur together more frequently than by chance. This information
can provide insights into potential genetic interactions and pathways.

7. • Random Forest: The Random
Forest algorithm excels at
classification tasks and feature
importance analysis. In the
realm of biology, it aids in
predicting gene functions,
identifying associations between
genes and phenotypes, and even
distinguishing between healthy
and disease states based on
intricate biological features.
Image courtesy:
https://images.app.goo.gl/vVGU8VrYV2wdYWq47

8. • Support Vector Machines (SVM):
SVM is employed to classify and
predict biological interactions,
such as protein-protein
interactions or drug-target
associations. By learning
patterns from known data, SVM
can predict potential
associations within biological
datasets.
Image courtesy:
https://www.javatpoint.com/machine-
learning-support-vector-machine-algorithm

9. • Deep Learning and Neural Networks:
Deep learning techniques, including
Convolutional Neural Networks (CNNs)
and Recurrent Neural Networks
(RNNs), have revolutionized biological
data analysis. CNNs excel at image
analysis, helping identify associations
by analyzing cellular structures, while
RNNs predict sequences, unveiling
relationships in genetic or protein data.
Image courtesy:https://images.app.goo.gl/1g2MjMhLG2E1Ttr56

10. • Bayesian Networks: With their
ability to model probabilistic
relationships, Bayesian networks
are invaluable for exploring
associations and dependencies
within biological data. These
networks can reveal causal
relationships between genes,
proteins, and diseases, offering
insights into regulatory networks.
Image courtesy:
https://images.app.goo.gl/GMMBBjqi1bQ65yC
KA

11. • Graph-based Methods:
Biological entities and their
relationships can be
represented as graphs, with
nodes representing entities
and edges representing
associations. Graph
algorithms, such as
clustering and centrality
analysis, help identify
modules and key entities,
uncovering associations
within intricate biological
networks.
Image courtesy:
https://encrypted-
tbn0.gstatic.com/images?q=tbn:ANd9GcT2GNEMFbiau
Yn_ccmOcy4TzMkCLdAHBVhozw&usqp=CAU

12. • Dimensionality Reduction
Techniques: Algorithms like Principal
Component Analysis (PCA) and t-
Distributed Stochastic Neighbor
Embedding (t-SNE) reduce the
dimensionality of high-dimensional
biological data. These techniques
can help visualize and identify
associations between samples or
variables.
Image courtesy:
https://www.geeksforgeeks.org/dimensionality-reduction/

13. • Association Rule Mining
Algorithms: Apart from
Apriori, other association
rule mining algorithms like
FP-Growth and Eclat are
used to uncover hidden
relationships between
biological entities. These
algorithms are particularly
useful in analyzing large-
scale genomic data.
Image courtesy:
https://images.app.goo.gl/EBMjBt9Uf7Q6zQKh9

14. • Enrichment Analysis: While not
a single algorithm, enrichment
analysis techniques like Gene
Ontology (GO) analysis or
pathway enrichment can reveal
associations between biological
entities based on their
functional annotations. These
methods help interpret the
biological significance of
associations.

15. • Transfer Learning: Transfer
learning involves leveraging
knowledge from one
biological context to make
predictions in another. It's
valuable for finding
associations across related
biological datasets and
adapting models trained on
one dataset to another.
Image courtesy:
https://images.app.goo.gl/m8DoqrDx7hociC8b7

16. CONCLUSION
• Machine learning algorithms offer diverse and powerful tools for uncovering
associations within biological data.
• By utilizing these algorithms, researchers can extract meaningful insights that
contribute to our understanding of biological processes, disease mechanisms,
and potential therapeutic targets.
• These algorithms, ranging from traditional techniques to deep learning models,
empower researchers to uncover associations that might otherwise remain
hidden in the intricate web of biological information.
• As technology advances, the synergy between machine learning and biological
research promises to reshape our comprehension of life's complexities, leading
to breakthroughs in medicine, agriculture, and beyond.

17. REFERENCE
Articles:
• https://www.sciencedirect.com
• https://www.researchgate.net
• https://academic.oup.com
Other resources:
• https://chat.openai.com

18. THANK YOU