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.

Mammography has proven to be the most effective tool for detecting breast cancer in its earliest and treatable stage. This paper investigates various phases of mammogram image analysis and different abnormality detection techniques in the mammogram. The present study’s primary aim is to apply the Multifractal theory for the analysis of digital mammograms. The mammogram images are x-raying images of the breast and grayscale in nature. The grayscale mammogram images are processed using image processing techniques and then analyzed by multifractal characteristics. Initially, simple thresholding used to avoid artefacts and noises in the mammograms taken from the MIAS dataset of 322 images. The thresholded images resulted in sharp edges and hence smoothened using a Gaussian filter of appropriate configuration. A novel feature extraction method is proposed based on multifractal spectral parameters. The multifractal characteristics of each mammogram plotted, and multifractal features extracted from the spectrum. The major multifractal features are the bandwidth of the spectrum, the height of the spectrum, maximum and minimum singularity exponent, peak singularity exponent, and the strength of multifractality. Out of the chosen multifractal features, the width of the spectrum, the height of the spectrum, minimum value for the strength of multifractality found to be the most significant features after conducting statistical analysis using ANOVA (Analysis of Variance). We propose a novel lesion localization method based on the extracted multifractal spectral parameters. The pectoral muscle present in the mammogram removed, and the mammogram image divided into four quadrants. The lesion or mass location in a specific quadrant is identified based on the variation in the quadrant’s multifractal characteristics. This method will reduce the search space of lesion in a single mammogram. Thus, the present study revealed that mammogram images exhibit multifractal behaviour. The multifractal parameters appear to be valuable biomarkers for quantitative assessment of breast tissue in the mammogram, which helps to diagnose breast cancer in its early stage.

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.