Shining a light on the dark proteome: Non‐canonical open reading frames and their encoded miniproteins as a new frontier in cancer biology
Z Posner, I Yannuzzi, JR Prensner - Protein Science, 2023 - Wiley Online Library
In the decades following the discovery that genes encode proteins, scientists have tried to
exhaustively and comprehensively characterize the human genome. Recent advances in …
exhaustively and comprehensively characterize the human genome. Recent advances in …
[图书][B] Bioinformatics: the machine learning approach
A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …
data. An unprecedented wealth of data is being generated by genome sequencing projects …
Prediction of human mRNA donor and acceptor sites from the DNA sequence
S Brunak, J Engelbrecht, S Knudsen - Journal of molecular biology, 1991 - Elsevier
Artificial neural networks have been applied to the prediction of splice site location in human
pre-mRNA. A joint prediction scheme where prediction of transition regions between introns …
pre-mRNA. A joint prediction scheme where prediction of transition regions between introns …
Prediction of probable genes by Fourier analysis of genomic sequences
S Tiwari, S Ramachandran, A Bhattacharya… - …, 1997 - academic.oup.com
Motivation: The major signal in coding regions of genomic sequences is a three-base
periodicity. Our aim is to use Fourier techniques to analyse this periodicity, and thereby to …
periodicity. Our aim is to use Fourier techniques to analyse this periodicity, and thereby to …
Using sampling and queries to extract rules from trained neural networks
MW Craven, JW Shavlik - Machine learning proceedings 1994, 1994 - Elsevier
Abstract Concepts learned by neural networks are difficult to understand because they are
represented using large assemblages of real-valued parameters. One approach to …
represented using large assemblages of real-valued parameters. One approach to …
Refinement of approximate domain theories by knowledge-based neural networks
GG Towell, JW Shavlik, MO Noordewier - Proceedings of the eighth …, 1990 - dl.acm.org
Standard algorithms for explanation-based learning require complete and correct
knowledge bases. The KBANN system relaxes this constraint through the use of empirical …
knowledge bases. The KBANN system relaxes this constraint through the use of empirical …
Improvements in protein secondary structure prediction by an enhanced neural network
DG Kneller, FE Cohen, R Langridge - Journal of molecular biology, 1990 - Elsevier
Computational neural networks have recently been used to predict the mapping between
protein sequence and secondary structure. They have proven adequate for determining the …
protein sequence and secondary structure. They have proven adequate for determining the …
Prediction of gene structure
We have developed a hierarchical rule base system for identifying genes in DNA
sequences. Atomic sites (such as initiation codons, stop codons, acceptor sites and donor …
sequences. Atomic sites (such as initiation codons, stop codons, acceptor sites and donor …
Covariation of mutations in the V3 loop of human immunodeficiency virus type 1 envelope protein: an information theoretic analysis.
BT Korber, RM Farber, DH Wolpert… - Proceedings of the …, 1993 - National Acad Sciences
The V3 loop of the human immunodeficiency virus type 1 (HIV-1) envelope protein is a
highly variable region that is both functionally and immunologically important. Using …
highly variable region that is both functionally and immunologically important. Using …
A hidden Markov model that finds genes in E.coli DNA
A Krogh, IS Mian, D Haussler - Nucleic acids research, 1994 - academic.oup.com
A hidden Markov model (HMM) has been developed to find protein coding genes in E. coli
DNA using E. coli genome DNA sequence from the EcoSeq6 database maintained by Kenn …
DNA using E. coli genome DNA sequence from the EcoSeq6 database maintained by Kenn …