ORCID Profile
0000-0003-3045-8584
Current Organisation
The University of Texas at Austin
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Hindawi Limited
Date: 26-03-2022
DOI: 10.1155/2022/9390539
Abstract: Background. Cancer risks vary in different BRCA1/2 mutations. We are interested in identifying regions associated with elevated/reduced risks of breast/ovarian cancers in the Chinese population and comparing with previously reported Caucasian-based breast/ovarian cancer cluster regions (OCCR/BCCR). We also aim to characterize the distribution and estimate the cancer risks of different Chinese recurrent mutations. Methods. A total of 3,641 cancer-free women and 4,278 female cancer patients were included in the study. Germline BRCA1/2 status was detected with licon-based next-generation sequencing. We calculated the odds ratio (OR) of breast cancer and OR of ovarian cancer, and their ratio of the two ORs (ROR) for each region. ROR indicated elevated odds of breast cancer and/or decreasing odds of ovarian cancer, and vice versa. The frequency, distribution, and penetrance of six known Chinese founder mutations were characterized, respectively. Haplotype analysis and age estimation were performed on the most prevalent founder mutation BRCA1: c.5470_5477del. Results. A total of 729 subjects were detected with germline BRCA1/2 deleterious mutations. The putative Chinese OCCR/BCCR partially overlapped with Caucasian-based OCCR/BCCR and shared structural-functional characteristics. The six known Chinese founder mutations greatly vary in both distribution and penetrance. The two widely spread mutations are estimated to convey low penetrance, while the area-restricted founder mutations seemed to confer higher/complete penetrance. BRCA1: c.5470_5477del is estimated to have emerged ∼2,090 years ago (70 B.C.) during the Han dynasty. Conclusions. BRCA1/2 carriers with different genotypes have significantly different cancer risks. An optimal risk assessment should be mutation specific, rather than concerning a single figure.
Publisher: Elsevier BV
Date: 12-2016
Publisher: Elsevier BV
Date: 07-2022
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: Omega-regular objectives in Markov decision processes (MDPs) reduce to reachability: find a policy which maximizes the probability of reaching a target set of states. Given an MDP, an initial distribution, and a target set of states, such a policy can be computed by most probabilistic model checking tools. If the MDP is only partially specified, i.e., some prob- abilities are unknown, then model-learning techniques can be used to statistically approximate the probabilities and enable the computation of the de- sired policy. For fully specified MDPs, reducing the size of the MDP translates into faster model checking for partially specified MDPs, into faster learning. We provide reduction techniques that al- low us to remove irrelevant transition probabilities: transition probabilities (known, or to be learned) that do not influence the maximal reachability probability. Among other applications, these reductions can be seen as a pre-processing of MDPs before model checking or as a way to reduce the number of experiments required to obtain a good approximation of an unknown MDP.
Publisher: IEEE
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
No related grants have been discovered for Sudarshanan Bharadwaj.