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1 gh rational design, have long been a goal of computational biology.
2 with Biopython, a broad-ranging toolkit for computational biology.
3 ristics is currently a foremost challenge in computational biology.
4 understanding life that today all biology is computational biology.
5 s is an important yet challenging problem in computational biology.
6 and risk prediction, imaging technology, and computational biology.
7 lusters, genetic-based risk, and imaging and computational biology.
8 e most important and challenging problems in computational biology.
9 protein sequences is an important problem in computational biology.
10 nce or structure is a fundamental problem in computational biology.
11 e is one of the most challenging problems in computational biology.
12 cent years in the area of bioinformatics and computational biology.
13 ithm, is a task of fundamental importance in computational biology.
14 cture alignments is of central importance in computational biology.
15 dden Markov models are widely applied within computational biology.
16 tivity and great importance in the growth of computational biology.
17 al of sequence comparison tools developed by computational biology.
18 rated approaches in genetics, proteomics and computational biology.
19 ch are routinely used in colloid science and computational biology.
20 of genes, has been an ongoing challenge for computational biology.
21 ifs continues to be a challenging problem in computational biology.
22 , is a fundamental challenge in genomics and computational biology.
23 alignment programs are an invaluable tool in computational biology.
24 stering is one of the most powerful tools in computational biology.
25 systematic studies of gene function aided by computational biology.
26 e homology detection is a central problem in computational biology.
27 ficity from sequence is an important goal in computational biology.
28 atory regions poses a challenging problem in computational biology.
29 ysis of graphical structures in genomics and computational biology.
30 on sequence homology is a central problem in computational biology.
31 A sequences remains a fundamental problem in computational biology.
32 ch are routinely used in colloid science and computational biology.
33 m a protein sequence is a grand challenge of computational biology.
34 he greatest challenges of bioinformatics and computational biology.
35 robabilistic-based approaches to problems in computational biology.
36 e sequence alignment is an important tool in computational biology.
37 protein sequences is an important problem in computational biology.
38 s one way to facilitate research progress in computational biology.
39 egarding the design and use of benchmarks in computational biology.
40 ackgrounds traditionally underrepresented in computational biology.
41 ries and genomic data is a common problem in computational biology.
42 ction in evolution, population genetics, and computational biology.
43 y and an increasing capacity for large-scale computational biology.
44 alyses, which constitute an integral part of computational biology.
45 neural network architectures for problems in computational biology.
46 ing an important topic in bioinformatics and computational biology.
47 ents (MSA) are emerging as powerful tools in computational biology.
48 mposite data patterns in many areas, such as computational biology.
49 ) is an important yet challenging problem in computational biology.
50 c biology is increasingly becoming a form of computational biology.
51 ols play a central role in several fields of computational biology.
52 cadian rhythms, etc) is a central problem in computational biology.
53 st new research directions in structural and computational biology.
54 -regulatory context is a fundamental goal in computational biology.
55 ped new ones to meet the changing demands of computational biology.
56 nomic fragments, is a challenging problem in computational biology.
57 expand their knowledge of bioinformatics and computational biology.
58 in the emerging fields of bioinformatics and computational biology.
59 emains one of the major unsolved problems in computational biology.
60 e bioinformatics and their use is central to computational biology.
61 pid mixing, mutagenesis, and structure-based computational biology.
62 common but difficult problem in present day computational biology.
63 remains one of the most widely used tools in computational biology.
64 on remains a difficult but important task in computational biology.
65 lding is one of the most difficult in modern computational biology.
66 s from the biomedical sciences, ecology, and computational biology.
67 ork problems that I designed for a course in computational biology.
68 lies functional similarity underlies much of computational biology.
69 gene expression profiles is a key problem in computational biology.
71 ed model by combining two methodologies from computational biology: a mechanics-based modeling of leu
72 nt and immediate solutions from the field of computational biology against Ebola, the International S
73 ection is one of the fundamental problems in computational biology, aiming to find protein sequences
74 ces in cell, developmental, evolutionary and computational biology allow Thompson's ideas to be integ
76 e papers cover a broad spectrum of topics in computational biology and bioinformatics, including DNA,
78 l Medicine (ISIBM), International Journal of Computational Biology and Drug Design and International
79 ed Medicine and the International Journal of Computational Biology and Drug Design in collaboration w
81 , the PIR has aspired to support research in computational biology and genomics through the compilati
86 CSD has a wide range of applications in both computational biology and population genomics analysis,
87 teins are very important to several areas of computational biology and provide an understanding of ph
89 ose an approach utilizing recent advances in computational biology and the wealth of publicly availab
90 approaches represent promising links between computational biology and well-established economic and
91 this review we outline previous advances in computational biology and, by tracing the steps involved
92 this review we outline previous advances in computational biology and, by tracing the steps involved
94 y evolving sciences of genomics, proteomics, computational biology, and complex system theory can be
95 biochemistry, and immunology to mathematics, computational biology, and engineering), initiatives, te
96 w that our combination of molecular biology, computational biology, and mathematical modeling is an e
99 ogy of infectious disease to bioinformatics, computational biology, and statistics--this exercise can
107 Finally, breakthroughs in structural and computational biology are beginning to unravel the mecha
109 sequence alignment plays a critical role in computational biology as it is an integral part in many
110 e fewer female authors on research papers in computational biology, as compared to biology in general
111 we talk to Robert Lowe, who is a Lecturer in computational biology at Queen Mary University of London
112 ternational Conference on Bioinformatics and Computational Biology (Biocomp), touch tomorrow's bioinf
113 ternational conference on Bioinformatics and Computational Biology (BIOCOMP07) was held in Las Vegas,
116 urce has supported research on evolution and computational biology by designing and compiling a compr
117 olecular evolution, functional genomics, and computational biology by maintaining a comprehensive, no
120 dynamics of DNA remains a major challenge in computational biology due to the dearth of precise exper
121 expression modelling is an important tool in computational biology due to the volume of high-throughp
123 n of data vectors represents a large part of computational biology, especially with the continuous in
124 ) is a new community data standard to encode computational biology experiments in a computer-readable
125 echnology underlying NGS is complex, and the computational biology expertise required to build system
126 works (TRNs) is of significant importance in computational biology for cancer research, providing a c
127 is award recognizes a leader in the field of computational biology for his or her significant contrib
128 motif finding is one of the core problems in computational biology, for which several probabilistic a
131 ress in genomics, structural proteomics, and computational biology, functional annotation of methyltr
132 ults suggest that the integrated strategy of computational biology, genomics, and genetics is a power
136 Advances in structure-function analyses and computational biology have enabled a deeper understandin
137 Recent advances in genome technologies and computational biology have facilitated genome-wide views
138 g, mass spectroscopy methods and advances in computational biology have greatly accelerated the disco
139 New developments in molecular immunology and computational biology have increased precision of donor
142 uthors than computer science papers, placing computational biology in between its two parent fields i
143 for Molecular Biology/European Conference on Computational Biology in Dublin, Ireland, in July 2015.
144 Our method has the potential to be used in computational biology in the analysis of DNA via tangle
145 g models common to research in many areas of computational biology, including cell signaling pathways
148 age, one of the most challenging problems in computational biology is how to effectively formulate th
149 nation of many completely sequenced genomes, computational biology is now faced with the challenge of
150 d web-based cloud computing are changing how computational biology is performed, who performs it, and
151 ly available, but often the limiting step in computational biology is the conversion of PDB structure
152 available, a major challenge in genomic and computational biology is to develop methods for comparin
155 As for the development of any modern drug, computational biology is uniquely positioned to contribu
156 homology detection, a fundamental problem in computational biology, is an indispensable step toward p
157 against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an im
158 blishment in 1997, International Society for Computational Biology (ISCB) has contributed importantly
159 blishment in 1997, International Society for Computational Biology (ISCB) has contributed importantly
160 s are part of many International Society for Computational Biology (ISCB) Student Council Regional St
161 gical Genome Project combines immunology and computational biology laboratories in an effort to estab
162 re designed by researchers with expertise in computational biology, limiting their accessibility to t
163 program begins with one week of training on computational biology methods development, transitions t
164 disciplines (e.g., biochemistry, omics, and computational biology; microbiology, immunology, and med
167 Synthetic biology leverages advances in computational biology, molecular biology, protein engine
171 es, in particular, deploying open-source for Computational Biology (OpenCB) software platform for sto
178 logy is an interdisciplinary field, and many computational biology research projects involve distribu
181 ation, such as the International Society for Computational Biology Student Council (ISCB-SC) and its
182 As part of the International Society for Computational Biology Student Council (ISCB-SC), Regiona
184 in part being engineered via new imaging and computational biology technologies, drawing upon literat
185 a transition to high-throughput genomics and computational biology that has also pervaded study of ma
186 In the current climate of high-throughput computational biology, the inference of a protein's func
187 Mobius has found numerous applications in computational biology to build and solve stochastic mode
189 deling, MD proves to be the gold standard in computational biology to investigate mechanistic details
191 hlamydomonas reinhardtii genome and advanced computational biology tools has allowed elucidation and
192 e, we use biochemical, biophysical, cell and computational biology tools to study two loss-of-functio
193 e engineering methods for the development of computational biology tools, and the need for new algori
194 n instances deeply seeded in data mining and computational biology, where high-throughput data captur
195 NA is applicable to many fields, including computational biology, where NA can guide the transfer o
196 ed DNA sequences is a fundamental problem in computational biology with important applications in fin
198 ological annotations is an important goal in computational biology, with protein function prediction
199 factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge.
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