Proteins. 2013 Nov;81(11):1885-99. doi: 10.1002/prot.24330. Epub 2013 Aug 16.
DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches.
Liu R, Hu J.
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streamsirjes
In the data mining field the classification of data stream creates many problems. The challenges
faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the
existing system focuses on the only first two challenges. We propose a framework in which each classifier is
prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation
and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the
novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction
for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the
concept of permutation and combination RBF kernel extracts the features and find out the relation between
them. This improves the novel class detect technique and provide more accuracy for classifying the data
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streamsirjes
In the data mining field the classification of data stream creates many problems. The challenges
faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the
existing system focuses on the only first two challenges. We propose a framework in which each classifier is
prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation
and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the
novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction
for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the
concept of permutation and combination RBF kernel extracts the features and find out the relation between
them. This improves the novel class detect technique and provide more accuracy for classifying the data
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Slides from tutorial on Deep Learning and Modern Natural Language Processing using Pytorch at PyData Miami 2019.
Note: These are the slides corresponding to the Jupiter Notebook tutorials which can be found in the following repo:
https://github.com/ZacharySBrown/deep-learning-nlp-pydata
Open science resources for `Big Data' Analyses of the human connectomeCameron Craddock
Neuroimaging has become a `Big Data' pursuit that requires very large datasets and high throughput computational tools. In this talk I will highlight many open science resources for acquiring the necessary data. This is from a lecture that I gave in 2015 at the USC Neuroimaging and Informatics Institute.
This proposed method focus on these issues by developing a novel classification algorithm by combining Gene Expression Graph (GEG) with Manhattan distance. This method will be used to express the gene expression data. Gene Expression Graph provides the optimal view about the relationship between normal and unhealthy genes. The method of using a graph-based gene expression to express gene information was first offered by the authors in [1] and [2], It will permits to construct a classifier based on an association between graphs represented for well-known classes and graphs represented for samples to evaluate. Additionally Euclidean distance is used to measure the strength of relationship which exists between the genes.
This is an introduction to a knowledge engineering methodology called 'Knowledge Engineering from Experimental Design' (KEfED). This methodology provides a powerful, intuitive method for modeling the design of scientific experiments and provides the foundation for work at the Biomedical Knowledge Engineering Group at the Information Sciences Institute (run by Gully Burns)
RNA-seq: A High-resolution View of the TranscriptomeSean Davis
The molecular microscopes that we use to examine human biology have advanced significantly with the advent of next generation sequencing. RNA-seq is one application of this technology that leads to a very high-resolution view of the transcriptome. With these new technologies come increased data analysis and data handling burdens as well as the promise of new discovery. These slides present a high-level overview of the RNA-seq technology with a focus on the analysis approaches, quality control challenges, and experimental design.
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Slides from tutorial on Deep Learning and Modern Natural Language Processing using Pytorch at PyData Miami 2019.
Note: These are the slides corresponding to the Jupiter Notebook tutorials which can be found in the following repo:
https://github.com/ZacharySBrown/deep-learning-nlp-pydata
Open science resources for `Big Data' Analyses of the human connectomeCameron Craddock
Neuroimaging has become a `Big Data' pursuit that requires very large datasets and high throughput computational tools. In this talk I will highlight many open science resources for acquiring the necessary data. This is from a lecture that I gave in 2015 at the USC Neuroimaging and Informatics Institute.
This proposed method focus on these issues by developing a novel classification algorithm by combining Gene Expression Graph (GEG) with Manhattan distance. This method will be used to express the gene expression data. Gene Expression Graph provides the optimal view about the relationship between normal and unhealthy genes. The method of using a graph-based gene expression to express gene information was first offered by the authors in [1] and [2], It will permits to construct a classifier based on an association between graphs represented for well-known classes and graphs represented for samples to evaluate. Additionally Euclidean distance is used to measure the strength of relationship which exists between the genes.
This is an introduction to a knowledge engineering methodology called 'Knowledge Engineering from Experimental Design' (KEfED). This methodology provides a powerful, intuitive method for modeling the design of scientific experiments and provides the foundation for work at the Biomedical Knowledge Engineering Group at the Information Sciences Institute (run by Gully Burns)
RNA-seq: A High-resolution View of the TranscriptomeSean Davis
The molecular microscopes that we use to examine human biology have advanced significantly with the advent of next generation sequencing. RNA-seq is one application of this technology that leads to a very high-resolution view of the transcriptome. With these new technologies come increased data analysis and data handling burdens as well as the promise of new discovery. These slides present a high-level overview of the RNA-seq technology with a focus on the analysis approaches, quality control challenges, and experimental design.
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
This paper presents a literature survey conducted for research oriented developments made till. The significance of this paper would be to provide a deep rooted understanding and knowledge transfer regarding existing approaches for gene sequencing and alignments using Smith Waterman algorithms and their respective strengths and weaknesses. In order to develop or perform any quality research it is always advised to conduct research goal oriented literature survey that could facilitate an in depth understanding of research work and an objective can be formulated on the basis of gaps existing between present requirements and existing approaches. Gene sequencing problems are one of the predominant issues for researchers to come up with optimized system model that could facilitate optimum processing and efficiency without introducing overheads in terms of memory and time. This research is oriented towards developing such kind of system while taking into consideration of dynamic programming approach called Smith Waterman algorithm in its enhanced form decorated with other supporting and optimized techniques. This paper provides an introduction oriented knowledge transfer so as to provide a brief introduction of research domain, research gap and motivations, objective formulated and proposed systems to accomplish ultimate objectives.
Deep learning methods applied to physicochemical and toxicological endpointsValery Tkachenko
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...Arinze Akutekwe
Comprehensive understanding of gene regulatory
networks (GRNs) is a major challenge in systems biology. Most
methods for modeling and inferring the dynamics of GRNs,
such as those based on state space models, vector autoregressive
models and G1DBN algorithm, assume linear dependencies
among genes. However, this strong assumption does not make
for true representation of time-course relationships across the
genes, which are inherently nonlinear. Nonlinear modeling
methods such as the S-systems and causal structure
identification (CSI) have been proposed, but are known to be
statistically inefficient and analytically intractable in high
dimensions. To overcome these limitations, we propose an
optimized ensemble approach based on support vector
regression (SVR) and dynamic Bayesian networks (DBNs). The
method called SVR-DBN, uses nonlinear kernels of the SVR to
infer the temporal relationships among genes within the DBN
framework. The two-stage ensemble is further improved by
SVR parameter optimization using Particle Swarm
Optimization. Results on eight insilico-generated datasets, and
two real world datasets of Drosophila Melanogaster and
Escherichia Coli, show that our method outperformed the
G1DBN algorithm by a total average accuracy of 12%. We
further applied our method to model the time-course
relationships of ovarian carcinoma. From our results, four hub
genes were discovered. Stratified analysis further showed that
the expression levels Prostrate differentiation factor and BTG
family member 2 genes, were significantly increased by the
cisplatin and oxaliplatin platinum drugs; while expression levels
of Polo-like kinase and Cyclin B1 genes, were both decreased by
the platinum drugs. These hub genes might be potential
biomarkers for ovarian carcinoma.
Implementation of energy efficient coverage aware routing protocol for wirele...ijfcstjournal
In recent years, wireless sensor network have been used in many application such as disaster reservation,
agriculture, environmental observation and forecasting .Coverage preservation and energy consumption
are two most important issues in wireless sensor networks. To increase the network lifetime, we propose an
energy efficient coverage aware routing protocol for wireless sensor network for randomly deployed sensor
nodes. Some of the routing protocol is based on energy efficiency and some are based on coverage aware.
The proposed routing protocol is based on both the issues i.e. coverage and energy, in which we first find
the k-mean i.e. the degree of coverage, so that we can use this in the selection of cluster heads in wireless
sensor network by using Genetic Algorithm for increasing network lifetime and coverage. For cluster head
selection each node evaluates its k-mean and energy by internal function which used as fitness function in
genetic algorithm. The proposed algorithm “Implementation of energy efficient coverage aware routing
protocol for Wireless Sensor Network” is designed for homogeneous wireless sensor network. Simulations
results show that proposed algorithm increases the network lifetime by reduce the energy consumption and
preserve coverage. Simulation is done with MATLAB and a comparison of algorithm with benchmark
algorithms is also performed.
Masters Thesis Defense: Minimum Complexity Echo State Networks For Genome and...Christopher Neighbor
Successful Thesis Defense presentation for my Master's in Electrical Engineering: Signal Processing and Machine Learning from Portland State University in March 2020.
http://www.ncbi.nlm.nih.gov/pubmed/20236959
J R Soc Interface. 2010 Sep 6;7(50):1341-54. doi: 10.1098/rsif.2010.0063. Epub 2010 Mar 17.
Topological network alignment uncovers biological function and phylogeny.
Kuchaiev O, Milenkovic T, Memisevic V, Hayes W, Przulj N.
http://www.ncbi.nlm.nih.gov/pubmed/19259413
Cancer Inform. 2008;6:257-73. Epub 2008 Apr 14.
Uncovering biological network function via graphlet degree signatures.
Milenković T, Przulj N.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
20131019 生物物理若手 Journal Club
1. DNABind: A hybrid algorithm for structure-based prediction of
DNA-binding residues by combining machine learning- and
template-based approaches. Proteins. 2013 Jun 5.
20131019
生物物理若手関西支部 Journal Club
4. Result: DNABind, a hybrid method of machine learning and template-based
approaches showed excellent performance on predicting DNA-binding residues.
Template
DNABind
EcoRV(1RVE:A)
CprK (3E6C:C)
Machine learning
True positive residues.
DNABind improves classification.
Query protein, Template protein, TP,
, FN
5. Aim
Protein-DNA interactions is important for cell biology.
Its determination by experiments is time- and cost-consuming.
Computational approaches are desirable.
6. Computational approaches
Data bank (PDB)
Binding residues characters
Exposed solvents
Higher electrostatics potential
More conserved
Hotspots as clusters of conserved residues
Structural properties (DNA-binding residue vs surface)
Packing density
Surface curvature
B-factor
Residue fluctuation
Hydrogen bond donor
http://www.rcsb.org/pdb/home/home.do
10. Features used in machine learning
Structure-based
PSSM (position specific scoring matrix)
Evolutionally conservation
Solvent accessibility
Local geometry (depth and protrusion index)
Topological features
degree, closeness, betweenness, clustering coefficient
Relative position (distance to centroid)
Statistical potential (Boltzmann distribution)
Sequence-based (more difficult than structure)
Amino acid identity
Residue physicochemical properties
polarity, secondary structure, molecular volume, codon diversity, electrostatic charge
Predicted structure (Not need 3D structure !!)
11. Features used in machine learning
Structure-based
PSSM
Relative solvent accessibility
Depth and protrusion index
Topological features
Distance to centroid
Statistical potentials
Sequence-based
PSSM
Predicted structures
Amino acid indices
Statistical potentials
Construct machine learning (SVM)
15. Network
Degree is a commonly used measure to reflect the local
connectivity of a node.
Closeness is a global centrality metric used to determine
how critical a residue is in a residue interaction network.
Betweenness of residue i is defined to be the sum of the
fraction of shortest paths between all pairs of residues
that pass through residue i.
Motif, hub, and community
are also important…
Clustering coefficient (transitivity) quantifies how close
its neighbors are to being a clique. Probability that the
adjacent vertices of a vertex are connected.
16. Network sample; human protein interactome
Scale-free
Small-world
Cluster
Power law (Pareto distribution)
Bioinformatics. 2012 Jan 1;28(1):84-90.
18. Machine learning
Support vector machine (SVM)
Decision tree
RandomForest
Logistic regression
LASSO (Elastic net and Ridge)
Neural networks (Deep learning)
Evolutionary algorithm
Gaussian processing
k nearest neighbor
Clustering
Bayesian networks
Association rule learning
Inductive logic programming (ILP)
19. Support vector machine (SVM)
Make hyperplane to divide groups.
Kernel method; non-linear to linear
Easy to do.
Much computational time.
Tuning is very difficult.
23. LASSO, Elastic net, and Ridge regression
Least Absolute Shrinkage and Selection Operator
LASSO
Elastic Net
Ridge
24. Neural networks
Artificial mammal brain (perceptron).
Hidden multi-layer.
Deep learning is hot topic!!
(hard to understand…)
http://opencv.jp/opencv-1.0.0/document/opencvref_ml_nn.html
25. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
26. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
Train data
27. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
Train data
28. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
Train data
29. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
Train data
30. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
Train data
31. n-fold cross validation
To evaluate how the results of a statistical analysis will
generalize to an independent data set.
Train data
Test 1
One-leave out CV
34. Statistical features of structure
A: Binding residues are highly solvent
accessible.
B, C: Binding residues have low depth and
high protrusion.
D-G: Not so much difference in networks.
H: Binding residues are less distant to the
centroid.
36. Performance
Higher TM score is required for good prediction.
TM-score is a measure of similarity between two protein structures with different tertiary
structures. < 0.2 is random relation and > 0.5 is highly related.
Proteins. 2004 Dec 1;57(4):702-10.
Nucleic Acids Res. 2005 Apr 22;33(7):2302-9.