BOHR Journal of Biocomputing and Nano Technology

Computer simulation of conformational motion of protein molecules using functionally specified models

2025-11-08T09:58:48+00:00

The article considers the problem of computer modeling of biological structures using functionally specified objects. Visualization is necessary for a researcher to understand the space and geometry of the object being studied. The dynamics of biological structures complicate the task. An example of a complex dynamic structure is the problem of protein folding. Efficient computational models are required to study the folding process. The aim of the work is to develop a reliable method for modeling molecular dynamics based on functionally specified objects. A method for modeling the conformational motion of protein molecules using functionally specified models is presented. The relationship between the structural organization of a protein and its functional activity, which manifests itself at the moments of protein transition from one conformation to another, is investigated. A mathematical model of the motion of protein molecules and an algorithm for conformational motion are described. An interactive modeling system is proposed based on the functional assignment of models. The test results showing the operability of the proposed model are shown. Based on the proposed method, new opportunities are opening up for research in the field of protein folding and the development of new protein structures. Protein models are also relevant for the development of self-twisting modular robots that can perform a specific task.

Principal directon divising partitioning initialisation of K-means clustering for discriminating among leukemias while identifying the most salient genes involved

2025-09-11T09:21:21+00:00

This paper attempts to cluster leukemia patients described by gene expression data and to discover the most discriminating genes that are responsible for the clustering. A combined approach of Principal Direction Divisive Partitioning (PDDP) and bisecting K-means algorithms is applied to the clustering of the investigated leukemia dataset. Both unsupervised and supervised methods are considered in order to get optimal results. The combination of PDDP and bisecting K-means successfully clusters leukemia patients and efficiently discovers salient genes able to discriminate the clusters. The combined approach works well on the automatic clustering of leukemia patients depending merely on the gene expression information, and it has great potential for solving similar problems, like classifying pancreatic tumors. The salient identified genes may thus enhance relevant information for discriminating among leukemias. A previous paper by us, cited in the references and in the paper, based on the same technique, was able to outperform a seminal paper on Science on their same data. In this paper, the bisection is iterated on more complex data in order to identify a tree of leukemias discriminated through their salient involved genes.

Principal direction devising partitioning initialization of K-means clustering for discriminating among ovarian cancers while identifying the most salient genes involved

2025-09-11T07:04:01+00:00

This paper aims to cluster ovarian cancer patients described by gene expression data in order to discover the most discriminating genes responsible for the clustering. A combined use of Principal Direction Divisive Partitioning (PDDP) and bisecting K-means algorithms is applied to the investigated ovarian cancer dataset. The cascading of PDDP and bisecting K-means does successfully cluster ovarian cancer subjects and efficiently discovers salient genes needed to discriminate such clusters. The combined approach worked well on the automatic clustering of ovarian cancer patients depending merely on the gene expression information, and it has great potential for solving similar problems, like classifying leukemias or pancreatic tumors. The saliently identified genes may thus enhance relevant information for discriminating among ovarian cancers. In conclusion, the approach is shown to be a powerful one even in a complex multifactorial case like the intricate ovarian cancer discrimination.