- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[Institution of Engineering and Technology Fifth Asia International Symposium on Mechatronics (AISM 2015) - Guilin, China (7-10 Oct. 2015)] Fifth Asia International Symposium on Mechatronics (AISM 2015) - Enhanced plasmonic effects in Ag decorated amorphous TiO2 nanotube arrays
摘要： The process of converting raw RNA sequencing (RNA-seq) data to interpretable results can be circuitous and time-consuming, requiring multiple steps. We present an RNA-seq mapping algorithm that streamlines this process. Our algorithm utilizes a hash table approach to leverage the availability and the power of high memory machines. SNAPR, which can be run on a single library or thousands of libraries, can take compressed or uncompressed FASTQ and BAM ?les, and output a sorted BAM ?le, individual read counts, and gene fusions, and can identify exogenous RNA species in a single step. SNAPR also does native Phred score ?ltering of reads. SNAPR is also well suited for future sequencing platforms that generate longer reads. We show how we can analyze data from hundreds of TCGA samples in a matter of hours while identifying gene fusions and viral events at the same time. With the reference genome and transcriptome undergoing periodic updates and the need for uniform parameters when integrating multiple data sets, there is great need for a streamlined process for RNA-seq analysis. We demonstrate how SNAPR does this ef?ciently and accurately.
关键词： computational biology,biology,biology computing,RNA.,genetic expression,Bioinformatics
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Observation of Dislocations in Graded Buffer Layers of IMM Single Junction InGaAs Solar Cells by Two-Photon Excitation Photoluminescence
摘要： In binary classi?cation, two-way confusion matrices, with corresponding measures, such as sensitivity and speci?city, have become so ubiquitous that those who review results may not realize there are other and more realistic ways to visualize data. This is, particularly, true when risk and reward considerations are important. The approach suggested here proposes that classi?cation need not offer a conclusion on every instance within a data set. If an algorithm ?nds instances (e.g., patient cases in a medical data set) in which attributes pertaining to a patient’s disease offer zero to nil information, there should be no classi?cation offered. From the physician’s perspective, disclosure of nil information should be welcome because it might prevent potentially harmful treatment. It follows from this that the developer of a classi?er can provide summary results amendable for helping the consumer decide whether or not it is prudent to pass or act (commission versus omission). It is not always about balancing sensitivity and speci?city in all cases, but optimizing action on some cases. The explanation is centered on John Kelly’s link of gambling with Shannon information theory. In addition, Graham’s margin of safety, Bernoulli’s utiles, and Hippocratic Oath are important. An example problem is provided using a Netherlands Cancer Institute breast cancer data set. Recurrence score, a popular molecular-based assay for breast cancer prognosis, was found to have an uninformative zone. The uninformative subset had been grouped with positive results to garner higher sensitivity. Yet, because of a positive result, patients might be advised to undergo potentially harmful treatment in the absence of useful information.
关键词： data compression,cancer,clinical diagnosis,sensitivity and speci?city,Data analysis,genetic expression,entropy
更新于2025-09-23 15:19:57
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[IEEE 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) - Vancouver, BC, Canada (2020.1.18-2020.1.22)] 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) - Direct Laser Writing of Titanium Dioxide-Laden Retinal Cone Phantoms
摘要： The process of converting raw RNA sequencing (RNA-seq) data to interpretable results can be circuitous and time-consuming, requiring multiple steps. We present an RNA-seq mapping algorithm that streamlines this process. Our algorithm utilizes a hash table approach to leverage the availability and the power of high memory machines. SNAPR, which can be run on a single library or thousands of libraries, can take compressed or uncompressed FASTQ and BAM ?les, and output a sorted BAM ?le, individual read counts, and gene fusions, and can identify exogenous RNA species in a single step. SNAPR also does native Phred score ?ltering of reads. SNAPR is also well suited for future sequencing platforms that generate longer reads. We show how we can analyze data from hundreds of TCGA samples in a matter of hours while identifying gene fusions and viral events at the same time. With the reference genome and transcriptome undergoing periodic updates and the need for uniform parameters when integrating multiple data sets, there is great need for a streamlined process for RNA-seq analysis. We demonstrate how SNAPR does this ef?ciently and accurately.
关键词： Bioinformatics,biology,genetic expression,computational biology,biology computing,RNA.
更新于2025-09-19 17:13:59
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[IEEE 2019 19th International Conference on Advanced Robotics (ICAR) - Belo Horizonte, Brazil (2019.12.2-2019.12.6)] 2019 19th International Conference on Advanced Robotics (ICAR) - Closed-Loop Control of a Magnetically Actuated Fiber-Coupled Laser for Computer-Assisted Laser Microsurgery
摘要： In binary classi?cation, two-way confusion matrices, with corresponding measures, such as sensitivity and speci?city, have become so ubiquitous that those who review results may not realize there are other and more realistic ways to visualize data. This is, particularly, true when risk and reward considerations are important. The approach suggested here proposes that classi?cation need not offer a conclusion on every instance within a data set. If an algorithm ?nds instances (e.g., patient cases in a medical data set) in which attributes pertaining to a patient’s disease offer zero to nil information, there should be no classi?cation offered. From the physician’s perspective, disclosure of nil information should be welcome because it might prevent potentially harmful treatment. It follows from this that the developer of a classi?er can provide summary results amendable for helping the consumer decide whether or not it is prudent to pass or act (commission versus omission). It is not always about balancing sensitivity and speci?city in all cases, but optimizing action on some cases. The explanation is centered on John Kelly’s link of gambling with Shannon information theory. In addition, Graham’s margin of safety, Bernoulli’s utiles, and Hippocratic Oath are important. An example problem is provided using a Netherlands Cancer Institute breast cancer data set. Recurrence score, a popular molecular-based assay for breast cancer prognosis, was found to have an uninformative zone. The uninformative subset had been grouped with positive results to garner higher sensitivity. Yet, because of a positive result, patients might be advised to undergo potentially harmful treatment in the absence of useful information.
关键词： data compression,cancer,clinical diagnosis,sensitivity and speci?city,Data analysis,genetic expression,entropy
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - In-band pumped composite Nd:YVO/Nd:GVO laser
摘要： In binary classi?cation, two-way confusion matrices, with corresponding measures, such as sensitivity and speci?city, have become so ubiquitous that those who review results may not realize there are other and more realistic ways to visualize data. This is, particularly, true when risk and reward considerations are important. The approach suggested here proposes that classi?cation need not offer a conclusion on every instance within a data set. If an algorithm ?nds instances (e.g., patient cases in a medical data set) in which attributes pertaining to a patient’s disease offer zero to nil information, there should be no classi?cation offered. From the physician’s perspective, disclosure of nil information should be welcome because it might prevent potentially harmful treatment. It follows from this that the developer of a classi?er can provide summary results amendable for helping the consumer decide whether or not it is prudent to pass or act (commission versus omission). It is not always about balancing sensitivity and speci?city in all cases, but optimizing action on some cases. The explanation is centered on John Kelly’s link of gambling with Shannon information theory. In addition, Graham’s margin of safety, Bernoulli’s utiles, and Hippocratic Oath are important. An example problem is provided using a Netherlands Cancer Institute breast cancer data set. Recurrence score, a popular molecular-based assay for breast cancer prognosis, was found to have an uninformative zone. The uninformative subset had been grouped with positive results to garner higher sensitivity. Yet, because of a positive result, patients might be advised to undergo potentially harmful treatment in the absence of useful information.
关键词： data compression,cancer,clinical diagnosis,sensitivity and speci?city,Data analysis,genetic expression,entropy
更新于2025-09-19 17:13:59