Transfer learning for drug–target interaction prediction

MotivationUtilizing AI-driven approaches for drug–target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach.ResultsHere, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets.Availability and implementationThe source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org

PATZ1 is a DNA damage-responsive transcription factor that inhibits p53 function

Insults to cellular health cause p53 protein accumulation, and loss of p53 function leads to tumorigenesis. Thus, p53 has to be tightly controlled. Here we report that the BTB/POZ domain transcription factor PATZ1 (MAZR), previously known for its tran- scriptional suppressor functions in T lymphocytes, is a crucial regulator of p53. The novel role of PATZ1 as an inhibitor of the p53 protein marks its gene as a proto-oncogene. PATZ1-deficient cells have reduced proliferative capacity, which we assessed by transcriptome sequencing (RNA-Seq) and real-time cell growth rate analysis. PATZ1 modifies the expression of p53 target genes associated with cell proliferation gene ontology terms. Moreover, PATZ1 regulates several genes involved in cellular adhesion and morphogenesis. Significantly, treatment with the DNA damage-inducing drug doxorubicin results in the loss of the PATZ1 transcription factor as p53 accumulates. We find that PATZ1 binds to p53 and inhibits p53-dependent transcription activation. We examine the mechanism of this functional inhibitory interaction and demonstrate that PATZ1 excludes p53 from DNA bind- ing. This study documents PATZ1 as a novel player in the p53 pathway

Derin Öğrenme Teknikleri Ve Ağ Analizi Yöntemleriyle Hazırlanmış Kapsamlı Biyomedikal İlişkiler Kaynağı

Deneysel biyomedikal arastırmalara yardımcı olmak için mevcut islemsel (computational) araçlar ve hizmetlerin, veri çesitliligi ve veri baglantılılıgı açılarından eksiklikleri bulundugundan dolayı, gerçek problemlere çözüm üretmek için kullanımları sınırlı kalmaktadır. CROssBAR projesinin amacı, ilaç kesfi problemi için çesitli biyomedikal kaynakları birbirine baglayarak ve yapay ve derin ögrenmeye dayalı tahminler yaparak kapsamlı bir hesaplama kaynagı ve bu kaynagı kullanan analiz araçları gelistirmektir. CROssBAR projesi kapsamında yeni ilaç kesfi için sanal tarama yapmak hedefiyle yapay ve derin ögrenme temelli tahmin yöntemleri ve modelleri tasarlayıp gelistirilmis, gelistirilmis olan modellerden elde edilen tahminler moleküler biyoloji deneyleri ile dogrulanmıs ve daha ileri analiz yapmak hedefi ile görsellestirme araçları olusturulmustur. Analiz ve görselleme araçları ag ve çizge temelli yöntemler kullanmakta ve ayrıca dogal olması nedeniyle verilerin görsellemesini iki boyuta yerlestirme (embedding) sayesinde gerçeklestirmektedir. Tahmin ve analiz yapabilmek için veri kaynakları-iliskisel veritabanı (CROssBAR) ve çizge veritabanı (SmartBioGraph), otomatik tahmin yapan araçlar (DEEPScreen ve MDeePred) ve görselleme araçları (iBioProVis, CROssBAR Web Hizmeti) tasarlanıp, gelistirilip gerçeklestirilmistir. Ayrıca, gerçeklestirilmis olan tahmin araçlarıyla yapılan tahminlerden bazı örnekler için moleküler biyoloji deneyleri tasarlanıp gerçeklestirilerek tahminlerin dogrulukları geçerlenmistir

Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm†

In this paper, Hematoxylin and Eosin (H&E) stained liver images are classified by using both Local Binary Patterns (LBP) and one dimensional SIFT (1-D SIFT) algorithm. In order to obtain more meaningful features from the LBP histogram, a new feature vector extraction process is implemented for 1-D SIFT algorithm. LBP histograms are extracted with different approaches and concatenated with color histograms of the images. It is experimentally shown that,with the proposed approach, it possible to classify the H&E stained liver images with the accuracy of 88 %

Classification of Human Carcinoma Cells Using Multispectral Imagery

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options

Contrast Enhancement of Microscopy Images Using Image Phase Information

Contrast enhancement is an important preprocessing step for the analysis of microscopy images. The main aim of contrast enhancement techniques is to increase the visibility of the cell structures and organelles by modifying the spatial characteristics of the image. In this paper, phase information-based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. Inspired by the groundbreaking design of the phase contrast microscopy (PCM), the proposed image enhancement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility. In addition, the concept of selective variation (SV) technique is introduced and enhancement parameters are optimized using SV. The experimental studies that were carried out on microscopy images show that the proposed scheme outperforms the baseline enhancement frameworks. The contrast enhanced images produced by the proposed method have comparable cellular texture structure as PCM images.This work was supported by the Turkish Ministry of Development under KanSiL_2016K121540 Project

Contrast Enhancement of Microscopy Images Using Image Phase Information

Contrast enhancement is an important preprocessing step for the analysis of microscopy images. The main aim of contrast enhancement techniques is to increase the visibility of the cell structures and organelles by modifying the spatial characteristics of the image. In this paper, phase information-based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. Inspired by the groundbreaking design of the phase contrast microscopy (PCM), the proposed image enhancement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility. In addition, the concept of selective variation (SV) technique is introduced and enhancement parameters are optimized using SV. The experimental studies that were carried out on microscopy images show that the proposed scheme outperforms the baseline enhancement frameworks. The contrast enhanced images produced by the proposed method have comparable cellular texture structure as PCM images.Turkiye Cumhuriyeti Kalkinma Bakanlig

Identification of Relative Protein Bands in Polyacrylamide Gel Electrophoresis (PAGE) Using a Multi-Resolution Snake Algorithm

In polyacrylamide gel electrophoresis (PAGE) image analysis, it is important to determine the percentage of the protein of interest of a protein mixture. This study presents reliable computer software to determine this percentage. The region of interest containing the protein band is detected using the snake algorithm. The iterative snake algorithm is implemented in a multi-resolutional framework. The snake is initialized on a low-resolution image. Then, the final position of the snake at the low resolution is used as the initial position in the higher-resolution image. Finally, the area of the protein is estimated as the area enclosed by the final position of the snake