Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.

Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in different cancer types. Some mutations dysregulate protein function so that their accumulation is responsible for cancer development and might be associated with different cancer types. Therefore, mutations as a feature set can be used as an informative candidate to distinguish various cancer types. There are several options for demonstrating mutations. One might employ binary values to demonstrate mutation regions. Another potential method for extracting features is utilizing mutation interpreters. In this study, we investigate the trinucleotide mutational pattern of each cancer type. Moreover, we extract salient NMF-based mutational signatures across various cancer types. Then, we identify cancer-associated genes of a target cancer based on its salient signatures. We evaluate the cancer-associated genes using survival and gene expression analysis in different stages of cancer. Furthermore, we introduce DiaDeL, which is a deep learning-based binary classifier. The DiaDeL model uses mutational signatures as input features and distinct a cancer type from the others. Our proposed model outperforms six state-of-the-art methods with 0.824 and 0.88 for accuracy and AUC, respectively. The source code is available at https://github.com/sabdollahi/DiaDeL.

Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vast number of existing variants and the existence of variants which are recognized as variant of uncertain significance (VUS). To tackle these challenges, we propose algorithms to assign a significance to each gene rather than each variant, describing its degree of pathogenicity. Since the interpreters identified most of the variants as VUS, most of the gene scores were identified as uncertain significance. To predict the uncertain significance scores, we design two matrix factorization-based models: the common latent space model uses genomics variant data as well as heterogeneous clinical data, while the single-matrix factorization model can be used when heterogeneous clinical data are unavailable. We have managed to show that the models successfully predict the uncertain significance scores with low error and high accuracy. Moreover, to evaluate the effectiveness of our novel input features, we train five different multi-label classifiers including a feedforward neural network with the same feature set and show they all achieve high accuracy as the main impact of our approach comes from the features. Availability: The source code is freely available at https://github.com/sabdollahi/CoLaSpSMFM.

Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein interaction might play different roles in diverse cancer types. Hence, prioritizing the PPIs in each cancer type would help detect cancer-associated pathways, find a better understanding of cancer biology, and facilitate drug discovery. Several studies to date have proposed computational methods for extracting the PPI essentiality of different cancer types based on the PPI network. The main drawback of these studies is not using a rich source such as genomics variant data. An amino acid sequence encodes useful information about protein structure and behavior. We represent each amino acid sequence based on its variants/mutations in seven different ways: binary vectors, pathogenicity scores, binding affinity changes upon mutations, gene expression-based network of the interactions, biophysicochemical properties, g-gap dipeptide, and one-hot vectors. Based on these representations, we design and consider seven different deep learning models. Then, we compare the accuracy of these models in predicting 20 different cancer types from the TCGA cohort. WinBinVec is a window-based model that outperforms the other models. Moreover, WinBinVec contains a PPI essentiality module that helps extract the essentiality probability of each PPI for every cancer type. Source code and Data: https://github.com/sabdollahi/WinBinVec.