Teresa H. Meng - Saratoga CA, US Wing H. Wong - Stanford CA, US Narges Asadi Bani - Stanford CA, US
Assignee:
The Board of Trustees of the Leland Stanford Junior University - Palo Alto CA
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
Circuits, devices and methods for processing learning networks are implemented using a variety of methods and devices. One example involves a circuit-implemented method to identify a relationship of objects in a set of objects. Local scores are generated for the object and possible parents. The local scores indicate relationship strength between object and parent. The results are stored in a memory. A state-machine circuit is used to perform sampling and searching of the parent sets for each data node. The local scores are used to encode orderings of the parent. An algorithm is executed that uses the encoded possible orderings and a random variable to generate and score a current order and a proposed order of the possible parent sets. The proposed orders are accepted or rejected based on probability rules applied to the scores for the current and proposed orders. Structures are sampled to assess a Bayesian-based relationship.
Methods And Systems For Assessment Of Clinical Infertility
Mylene W.M. Yao - Stanford CA, US Wing H. Wong - Stanford CA, US
International Classification:
A61B 17/43
US Classification:
600 33
Abstract:
Methods and computer-based systems for facilitating assessment of clinical infertility are provided. The methods and systems can be implemented to, for example, facilitate assessment of a subject for an in vitro fertilization treatment cycle, including determining probability of a live birth event. The methods and systems can be implemented to, for example, facilitate a determination of success implantation of embryos, selection of an optimal number of embryos to transfer, and determination of success in subsequent in vitro fertilization treatment cycles following an unsuccessful treatment cycle.
Methods For Unsupervised Learning Using Optional Pólya Tree And Bayesian Inference
Li Ma - Stanford CA, US Wing H. Wong - Stanford CA, US
Assignee:
The Board of Trustees of the Leland Srandford Junior University - Palo Alto CA
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
The present disclosure describes an extension of the Pólya Tree approach for constructing distributions on the space of probability measures. By using optional stopping and optional choice of splitting variables, the present invention gives rise to random measures that are absolutely continuous with piecewise smooth densities on partitions that can adapt to fit the data. The resulting optional Pólya tree distribution has large support in total variation topology, and yields posterior distributions that are also optional Pólya trees with computable parameter values.
Wing H. Wong - Stanford CA, US Hong Yang - Stanford CA, US
International Classification:
C40B 30/00 C07H 1/08
US Classification:
506 7, 536 254
Abstract:
The present disclosure provides methods for determining phased nucleic acid sequence for a single chromosome of interest and/or a single chromosomal fragment of interest. The present disclosure also provides methods for determining phased nucleic acid sequence for a plurality of single chromosomes of interest and/or a plurality of single chromosomal fragments of interest. The plurality of single chromosomes of interest may be of one or more chromosome types. The present disclosure also provides a method for isolating a plurality of chromosomal fragments of a specified size range, where the chromosomal fragments are from one or more specified regions of the genome. The plurality of chromosomal fragments may be separated into single chromosomal fragments and sequenced to provide phased nucleic acid sequence for the single chromosomal fragments. Alternatively, the plurality of chromosomal fragments may be sequenced together to provide unphased nucleic acid sequence for the chromosomal fragments.
Method And System For Phasing Individual Genomes In The Context Of Clinical Interpretation
Hua Tang - Stanford CA, US Michael Snyder - Stanford CA, US Jennifer Li-Pook-Than - Menlo Park CA, US Konrad J. Karczewski - Stanford CA, US Nicholas Johnson - Palo Alto CA, US Wing H. Wong - Stanford CA, US
Assignee:
The Board of Trustees of the Leland Stanford Junior University - Palo Alto CA
International Classification:
G06F 19/00
US Classification:
703 2, 703 11
Abstract:
The present disclosure presents a unified system to phase a personal genome for downstream clinical interpretation. In an embodiment, an initial phasing is generated using public datasets, such as haplotypes from the 1000 Genomes Project, and a phasing toolkit. A local perturbation algorithm is applied to improve long range phasing. If available, a Mendelian inheritance pipeline is applied to identify phasing of novel and rare variants. These datasets are merged, followed by correction by any experimental data. This allows for full clinical interpretation of the role of a group of variants in a gene, whether inherited or de novo variants.
Method For Generating Healthcare-Related Validated Prediction Models From Multiple Sources
Wing H. Wong - Stanford CA, US Bokyung Choi - San Francisco CA, US
Assignee:
UNIVFY INC. - Los Altos CA
International Classification:
G06Q 50/22
US Classification:
705 2
Abstract:
Provided is a method for generating prediction models from multiple healthcare centers. The method allows a third party to use data sets from multiple sources to build prediction models. By entering the data sets in a Model Deconstruction and Transfer (MDT) platform, a healthcare center may provide data to a third party without the need to de-identify data or to physically transfer any identifying or de-identified data from the healthcare center. The MDT platform includes a variable library, which allows the healthcare center to select variables that will be used to generate and validate the prediction model. Also provided is a method for compensating sources that contribute data sets based upon the percentage of clinical data that is used to generate a prediction model.
Method And System For Accurate Construction Of Long Range Haplotype
Nicholas Johnson - Palo Alto CA, US Wing H. Wong - Stanford CA, US Hua Tang - Stanford CA, US
Assignee:
The Board of Trustees of the Leland Stanford Junior University - Palo Alto CA
International Classification:
G06F 19/22
US Classification:
702 20
Abstract:
In an embodiment of the present invention, a modified version of the PHASE model is implemented that is substantially more accurate than the FastPHASE model. Modifications in an embodiment of the present invention include using a parameterization EM algorithm similar to that of the FastPHASE model, and to perform optimization on haplotypes rather than MCMC sampling. In an embodiment, the imputed haplotypes themselves are used as hidden states in the HMM because this is believed to be important for the PHASE model's accuracy. This increase in accuracy becomes more pronounced with increasing sample size. This difference is attributed to the PHASE model's likelihood which produces long, shared haplotypes between pairs of individuals.
Thomas S. W. Wong - San Francisco CA Wing Y. Wong - Sunnyvale CA Edwin M. W. Chow - Sunnyvale CA
Assignee:
National Semiconductor Corporation - Santa Clara CA
International Classification:
H03K 1760 H03K 508
US Classification:
307296R
Abstract:
A power up circuit for an associated digital circuit is disclosed which prevents noise from falsely resetting the associated digital circuit after completion of the powering up function. The power up circuit has a series connection of a first capacitor and a high impedance coupled between the power supply and ground. The high impedance is connected in parallel with the source and drain electrodes of an enhancement type field effect transistor. The node of the high impedance and the first capacitor is connected to an inverting amplifier which produces an inverted output after the input signal falls to a threshold potential. The output of the inverting amplifier is connected to the gate of the field effect transistor and to a second capacitor which is connected to ground. The output of the inverting amplifier is applied to the reset line of the associated digital circuit to cause resetting of the digital circuit upon the connection of the power supply to the digital circuit. After powering up, the field effect transistor is biased on by the output of the inverting amplifier.
Bright Roll, Inc. - Operations Engineer (2012) Glam Media, Inc. - Linux/Operations Systems Administration (2009-2012) VMware - Sr. Systems Administrator (2006-2009)
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Married
About:
I'm an IT Guy who loves photography, Anime, Manga, and J-pop. A long time resident of California, I occasionally travel to other states and find myself marvelling at the differences in culture, as...
Wing Wong
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Wing Wong
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