Email: usamarajput205@gmail.com

1. Neural Networks
BY: USAMA AAMIR

2. A neuron is a cell that carries electrical impulses. Neurons are the basic
( functional & structural ) units of our nervous system.
Introduction

3. Introduction
Neural Network are simulations loosely patterned after biological neurons.
Neural networks are constructed to resemble the behavior of human brains
(neurons).
Characterizes the ability to learn, recall, and generalize from training
patterns.

5. Training of Neural NetworkBefore using any NN model, it must be
trained with representative data. There are basically two types of training;
supervised and unsupervised.
Test data Test data is a separate dataset, which is used to test the trained
NN to determine whether the NN has generalized the training data set
accurately
Data preparation sometimes it is useful to scale data before training.
This will improve the training process.

8. Different Types of Neural Networks
• Feedforward Neural Network – Artificial Neuron.
• Radial Basis Function Neural Network.
• Multilayer Perceptron.
• Convolutional Neural Network.
• Recurrent Neural Network (RNN) – Long Short Term Memory.
• Modular Neural Network.
• Sequence-To-Sequence Models.

9. Applications in Bioinformatics
• Coding region recognition and gene identification.
• Recognition of transcription and translational signals.
• Sequence feature analysis and classification.
• Protein structure prediction.
• Prediction of signal peptides.

10. Advantages
• ANNs have the ability to learn and model non-linear and complex
relationships, which is really important because in real-life, many of the
relationships between inputs and outputs are non-linear as well as
complex.
• ANNs can generalize — After learning from the initial inputs and
their relationships, it can infer unseen relationships on unseen data as
well, thus making the model generalize and predict on unseen data.

11. References
• Hapudeniya, M., Muditha, D. Artificial Neural Networks in Bioinformatics. Sri Lanka Journal of
Bio-Medical Informatics 2010;1(2):104-111.
• Matthew J. Wood1, Jonathan D. Hirst. RECENTAPPLICATIONSOFNEURAL
NETWORKSINBIOINFORMATICS. Journal of Biological and Artificial Intelligence Environments,
91–97.
• MALADKAR, K. (2018). 6 Types of Artificial Neural Networks Currently Being Used in Machine
Learning. Retrieved from https://www.analyticsindiamag.com/6-types-of-artificial-neural-networks-
currently-being-used-in-todays-technology/
• Jiaconda. (2016). A Concise History of Neural Networks. Retrieved from
https://towardsdatascience.com/a-concise-history-of-neural-networks-2070655d3fec