收费全文 | 6309篇 |
免费 | 555篇 |
国内免费 | 13篇 |
6877篇 |
2024年 | 7篇 |
2023年 | 66篇 |
2022年 | 132篇 |
2021年 | 223篇 |
2020年 | 101篇 |
2019年 | 162篇 |
2018年 | 178篇 |
2017年 | 174篇 |
2016年 | 225篇 |
2015年 | 328篇 |
2014年 | 323篇 |
2013年 | 414篇 |
2012年 | 505篇 |
2011年 | 458篇 |
2010年 | 291篇 |
2009年 | 225篇 |
2008年 | 322篇 |
2007年 | 327篇 |
2006年 | 277篇 |
2005年 | 272篇 |
2004年 | 204篇 |
2003年 | 184篇 |
2002年 | 224篇 |
2001年 | 148篇 |
2000年 | 191篇 |
1999年 | 129篇 |
1998年 | 50篇 |
1997年 | 28篇 |
1996年 | 33篇 |
1995年 | 46篇 |
1994年 | 34篇 |
1993年 | 32篇 |
1992年 | 69篇 |
1991年 | 58篇 |
1990年 | 58篇 |
1989年 | 62篇 |
1988年 | 44篇 |
1987年 | 33篇 |
1986年 | 41篇 |
1985年 | 44篇 |
1984年 | 25篇 |
1983年 | 21篇 |
1982年 | 12篇 |
1981年 | 16篇 |
1980年 | 8篇 |
1979年 | 15篇 |
1978年 | 12篇 |
1977年 | 12篇 |
1976年 | 7篇 |
1972年 | 6篇 |
Background
Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics.Results
We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy.Conclusion
HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp.The cooperation of Bacillus subtilis strain DKT and Comamonas testosteroni KT5 was investigated for biofilm development and toluenes and chlorobenzenes degradation. Bacillus subtilis strain DKT and C. testosteroni KT5 were co-cultured in liquid media with toluenes and chlorobenzenes to determine the degradation of these substrates and formation of dual-species biofilm used for the degradation process. Bacillus subtilis strain DKT utilized benzene, mono- and dichlorinated benzenes as carbon and energy sources. The catabolism of chlorobenzenes was via hydroxylation, in which chlorine atoms were replaced by hydroxyl groups to form catechol, followed by ring fission via the ortho-cleavage pathway. The investigation of the dual-species biofilm composed of B. subtilis DKT and C. testosteroni KT5 (a toluene and chlorotoluene-degrading isolate with low biofilm formation) showed that B. subtilis DKT synergistically promoted C. testosteroni KT5 to develop biofilm. The bacterial growth in dual-species biofilm overcame the inhibitory effects caused by monochlorobenzene and 2-chlorotoluene. Moreover, the dual-species biofilm showed effective degradability toward the mixture of these substrates. This study provides knowledge about the commensal relationships in a dual-culture biofilm for designing multispecies biofilms applied for the biodegradation of toxic organic substrates that cannot be metabolized by single-organism biofilms.
相似文献