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About 10,000 papers for “Bioinformatics”

New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics

Majid Mokhtari, Samane Khoshbakht, Kobra Ziyaei et al. · Briefings in Bioinformatics · 2024

Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.

Cited by 19Open access

BIOINFORMATICS Editorial OPEN BIOINFORMATICS

2008

Many wonderful algorithms are published each month in Bioinformatics and other computational biology journals. Most of these algorithms are made freely available to the rest of the bioinformatics community. However, there is a growing trend to make new software available only through a HTML interface on the World-Wide Web, or via a closed, proprietary binary distribution. This trend will ultimately inhibit the growth of our discipline. As a community, we need to dedicate ourselves to the free and open exchange of algorithms and software. Our journals recognize the need for freely exchanged ideas: Bioinformatics requires that software be available for two years. However, the mechanism of making programs available to the rest of the community is not specified. The choice is rightly left up to the researcher. But some choices are better than others, and it is important to be sure that we make the choice that will best advance our community as well as our careers. The most forthright approach to sharing software is through the use of Open Source. The Open Source initiative makes the original source code available to other developers via a license that allows for free redistribution and derived works while preserving the integrity of the author’s original source code (see

Cited by 0

BIOINFORMATICS Review Structural bioinformatics

Svava Ósk Jónsdóttir, Flemming Steen Jørgensen, Søren Brunak · 2008

Vol. 21 no. 10 2005, pages 2145–2160 doi:10.1093/bioinformatics/bti314 Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

Cited by 0

BIOINFORMATICS REVIEW doi:10.1093/bioinformatics/btm323 Structural bioinformatics

2007

Motivation: Proteins function through interactions with other proteins and biomolecules. Protein–protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of its applications and project future developments is in order. Summary: Interface prediction methods rely on a wide range of sequence, structural and physical attributes that distinguish interface residues from non-interface surface residues. The input data are manipulated into either a numerical value or a probability represent-ing the potential for a residue to be inside a protein interface. Predictions are now satisfactory for complex-forming proteins that are well represented in the Protein Data Bank, but less so for under-represented ones. Future developments will be directed at tackling problems such as building structural models for multi-component structural complexes. Contact:

Cited by 0