Avleen Malhi's research while affiliated with Aalto University and other places
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Publications (7)
Identification of genes that lead other genes towards disease with neurological disorders like Parkinson's disease (PD) is an important factor in biomedical research. Machine learning techniques have been extensively used in recent years for effective identification of genes associated with the disease. However, the data used in these methods were...
The most challenging issue in diagnosing and treating neurological disorders is gene identification that causes the disease. Classification of the genes that cause or initiate different genes leading to diseases with neurological disorders like Parkinson’s disease, is a grave challenge in biomedical research. Detecting neurological disorders has a...
Correlation determination brings out relationships in data that had not been seen before and it is imperative to successfully use the power of correlations for data mining. In this paper, we have used the concepts of correlations to cluster data, and merged it with recommendation algorithms. We have proposed two correlation clustering algorithms (R...
Parkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to id...
Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image fea...
Citations
... PD is characterized by its progressive nature and is a condition that gradually advances over time. Early detection of the illness offers an opportunity for effective management with appropriate medicine, perhaps leading to its eradication [16,17]. Figure 1 displays the symptoms of PD. ...
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... Publications Percentage CNN [26,32,36,46,47,49,53,56,61,62,64,65,70,71,74,77,81,[86][87][88][89]98,99,[101][102][103][104]109,111,113,114] 33.33% DNN [27,63,93,108] 0.04% KNN [28,31,32,41,89,107] 0.06% XGBoost [27,28,35,38,39,41,44,54,58,73,79,80,89,106] 0.15% SVM [25,28,38,51,63,64,74,80,83,88,106,114] 0.13% VGG-16 [26,30,32,53,62,75,78,81,87,93,97,105] 0.13% ResNet [26,30,34,36,40,75,81,85,87,102,104,109,112] 0.14% Decision Tree [28,44,54,67,79,80,91,106,107] 0.1% Random Forest [25,28,31,38,44,54,58,80,91,92,95,106,107] 0.13% Ada Boost [28,54,58,60] 0.04% LRP [46,58,78,98,100,119] 0.06% DeepLIFt [33,98,100,119] 0.04% GBP [98,100,119] 0.03% MLP [31,64,111] 0.03% ReLU [33,77,82,101] 0.04% ANN [58,88] 0.02% Logistic Regression [44,106,107] 0.03% ANFIS, COBA, CUBA [86] 0.01% Naive Bayes [106] 0.01% SeNet [36] 0.01% CovNet [76] 0.01% U-Net [76,96] 0.02% Dense-Sharp, APN, NSAM [43] 0.01% TabNet, DFS, Bayesian Network [48] 0.01% ChexNet [70] 0.01% CIU [113] 0.01% ...
... This research also used the Liu and Wang index as an interbicluster evaluation function to see the biclustering algorithm's performance in measuring the similarity between biclusters. The Liu and Wang index can see the likeness of each bicluster group from each minimum combination of rows and columns formed (Liu & Wang 2007) The higher the Liu and Wang index, the more similar the bicluster with the same membership characteristics formed will become (Lee et al., 2011;Al-Akwaa, 2012;Peng et al., 2014;Pandove & Malhi, 2021). ...
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... In recent years, Artificial Intelligent (AI) techniques have been utilized in finding genetic markers associated with diseases, though not on PTSD. For instance, to classify whether a gene is associated with Parkinson's Disease (PD), a neural network-based ensemble (n-semble) method based on protein features has been put forward, reaching 88.9%, 90.9% and 89.8% for the precision, recall and F score in a five-fold validation, respectively 61 . Another PD-related gene prediction model named N2A-SVM has also been proposed based on protein interaction information and techniques including Node2vec, the autoencoder and the support vector machine 62 . ...
