2,487 research outputs found
Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements
In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques
Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance
Fractal feature selection model for enhancing high-dimensional biological problems
The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%
Online semi-supervised learning in non-stationary environments
Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and
balanced data, immediately or after some delay, to extract worthwhile knowledge from the
continuous and rapid data streams. However, in many real-world applications such as
Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer
Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of
Things sensors and real-time data on the Internet. Manual labelling of these data streams
is not practical due to time consumption and the need for domain expertise. Another
challenge is learning under Non-Stationary Environments (NSEs), which occurs due to
changes in the data distributions in a set of input variables and/or class labels. The problem
of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms
have no access to the true class labels directly when the concept evolves. Several approaches
exist that deal with NSE and EVL in isolation. However, few algorithms address both issues
simultaneously. This research directly responds to ILNSE’s challenge in proposing two
novel algorithms “Predictor for Streaming Data with Scarce Labels” (PSDSL) and
Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label
scarcity issues in online machine learning.
The key capabilities of PSDSL include learning from a small amount of labelled data in an
incremental or online manner and being available to predict at any time. To achieve this,
PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it
continuously learns from incoming data and updates the model as new labelled or
unlabelled data becomes available over time. Furthermore, it can predict under NSE
conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier,
which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch
and adapt to the conditions. The PSDSL adapts to learning states between self-learning,
micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of
the data stream. HDWM makes use of “seed” learners of different types in an ensemble to
maintain its diversity. The ensembles are simply the combination of predictive models
grouped to improve the predictive performance of a single classifier.
PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification
on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than
existing approaches on most real-time data streams including randomised data instances.
PSDSL performed significantly better than ‘Static’ i.e. the classifier is not updated after it is
trained with the first examples in the data streams. When applied to MOA-generated data
streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC,
while SCARGC performed the same as the Static. PSDSL achieved better average prediction
accuracies in a short time than SCARGC.
The HDWM algorithm is evaluated on artificial and real-world data streams against existing
well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic
DWM algorithm. The results showed that HDWM performed significantly better than WMA
and DWM. Also, when recurring concept drifts were present, the predictive performance of
HDWM showed an improvement over DWM. In both drift and real-world streams,
significance tests and post hoc comparisons found significant differences between
algorithms, HDWM performed significantly better than DWM and WMA when applied to
MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The
seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms
benefit from the use of both forgetting and retaining the models. The algorithm also
provides the independence of selecting the optimal base classifier in its ensemble depending
on the problem.
A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts
during the cluster labelling process. In this process, PSDSL transforms the centroids’
information of micro-clusters into micro-instances and generates new clusters called
Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and
successfully guide the cluster labelling process after the concept drifts in the absence of true
class labels. PSDSL has been evaluated on real-world problem ‘keystroke dynamics’, and
the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC
(81.6%), while the Static (49.0%) significantly degrades the performance due to changes in
the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found
highly fluctuated between (41.1% to 81.6%) based on different values of parameter ‘k’
(number of clusters), while PSDSL automatically determine the best values for this
parameter
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection
The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using a combination of genetic markers and advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, machine learning algorithms and high-performance computing was employed. First, genetic markers highly associated with disease resistance were identified using next-generation sequencing data and statistical analysis. Then, an adiabatic quantum machine learning algorithm was developed to integrate these markers into a single predictor of disease susceptibility. The results demonstrate that the integrative use of genetic markers and adiabatic quantum machine learning significantly improved the accuracy and efficiency of disease resistance-based marker-assisted plant selection. By leveraging the power of adiabatic quantum computing and genetic markers, more effective and efficient strategies for disease resistance-based marker-assisted plant selection can be developed
- …