Pournami P N

The past few years have witnessed machine learning techniques take the limelight in multiple research domains. One such domain that has reaped the benefits of machine learning is computer-aided drug discovery, where the search space for candidate drug molecules is decreased using methods such as virtual screening. Current state-of-the-art sequential neural network models have shown promising results and we would like to replicate similar results with virtual screening using the encoded molecular information known as simplified molecular-input line-entry system (SMILES). Our work includes the use of attention-based sequential models — the long short-term memory with attention and an optimized version of the transformer network specifically designed to deal with SMILES (ChemBERTa). We also propose the “Overall Screening Efficacy”, an averaging metric that aggregates and encapsulates the model performance over multiple datasets. We found an overall improvement of about 27% over the benchmark model, which relied on parallelized random forests.

Antony, JisnaPenikalapati, AkhilReddy, J. Vinod KumarPournami, P. N.Jayaraj, P. B.Determining the native structure of a protein, given its primary sequence is one of the most demanding tasks in computational biology. Traditional protein structure prediction methods are laborious and involve vast conformation search space. Contrarily, deep learning is a rapidly evolving field with outstanding performance at problems where there are complicated relationships between input features and desired outputs. Various deep neural network architectures such as recurrent neural networks, convolution neural networks, deep feed-forward neural networks are becoming popular for solving problems in protein science. This work mainly concentrates on prediction of three-dimensional structure of proteins from the given primary sequences using deep learning techniques. Long short-term memory (LSTM) and bidirectional LSTM (BLSTM) neural network architectures are used for predicting protein tertiary structures from primary sequences. The result shows that single-layer BLSTM networks fed with primary sequence and position-specific scoring matrix data gives better accuracy compared to LSTM and two-layer BLSTM models. This study may get benefited to the computational biologists working in the area of protein structure prediction.

Image registration is an indispensible task required in many image processing applications, which geometrically aligns multiple images of a scene, with differences caused due to time, viewpoint or by heterogeneous sensors. Feature-based registration algorithms are more robust to handle complex geometrical and intensity distortions when compared to area-based techniques. A set of appropriate geometrically invariant features forms the cornerstone for a feature-based registration framework. Feature point or interest point detectors extract salient structures such as points, lines, curves, regions, edges, or objects from the images. A novel interest point detector is presented in this paper. This algorithm computes interest points in a grayscale image by utilizing a graph centrality measure derived from a local image network. This approach exhibits superior repeatability in images where large photometric and geometric variations are present. The practical utility of this highly repeatable feature detector is evident from the simulation results.

The registration process in the medical field has evolved rapidly making the physicians to rely on computer algorithms for processing and diagnosing the diseases. Registration has been used for studying disease growth and treatment responses of the diseases. Brain image registration enables physicians to diagnose diseases like Alzheimer's based on the changes in the internal structure of the brain. One of the main challenges in this topic is the huge computational requirements of the registration processes. In this paper we propose a method for the fast and accurate registration of MR brain images using the shape property of the axial slice of the brain. The algorithm exhibits reduced computational time when compared to a standard existing approach, which makes it useful for real time applications.