Convolutional neural network based data interpretable framework for Alzheimer's treatment planning.

Alzheimer's disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data.

An Interpretable Framework for Identifying Cerebral Microbleeds and Alzheimer's Disease Severity using Multimodal Data.

Cerebral microbleeds (CMBs) are tiny chronic brain haemorrhages that have been recognised as prognostic indicators for a number of acute cerebrovascular disorders, such as stroke, traumatic disorder, and Alzheimer's disease. For early-stage chronic disease diagnosis, it is challenging to automate the detection of CMBs and increase the reliability of prediction outputs. This study developed a system for identifying microbleeds in MRI images and gene expression data and determining the severity of Alzheimer's disease (AD).

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images.

Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN.

StrucBreak: A Computational Framework for Structural Break Detection in DNA Sequences.

Damages or breaks in DNA may change the characteristics of genomes and causes various diseases. In this work we construct a system that incorporates the maximum likelihood-based probabilistic formula to assess the number of damages that have occurred in any DNA sequence. This approach has been progressively benchmarked by implementing simulated data sets so that the outcomes can be compared with a ground truth or reference value.

MSuPDA: A Memory Efficient Algorithm for Sequence Alignment.

Space complexity is a million dollar question in DNA sequence alignments. In this regard, memory saving under pushdown automata can help to reduce the occupied spaces in computer memory. Our proposed process is that anchor seed (AS) will be selected from given data set of nucleotide base pairs for local sequence alignment.

Performance evaluation of Warshall algorithm and dynamic programming for Markov chain in local sequence alignment.

Markov Chain is very effective in prediction basically in long data set. In DNA sequencing it is always very important to find the existence of certain nucleotides based on the previous history of the data set. We imposed the Chapman Kolmogorov equation to accomplish the task of Markov Chain.