Discovering　functional　gene　clusters　based　on　gene　expression　data　has　been　a　widely-used　method　that　offers　a　tremendous　opportunity　for　understanding　the　functional　genomics　of　a　specific　disease.　Due　to　its　strong　power　of　comprehending　and　interpreting　mass　of　genes,　plenty　of　studies　have　been　done　on　detecting　and　analyzing　the　gene　clusters　for　various　diseases.　However,　more　and　more　evidence　suggest　that　human　diseases　are　not　isolated　from　each　other.　Therefore,　it＇s　significant　and　interesting　to　detect　the　common　functional　gene　clusters　driving　the　core　mechanisms　among　multiple　related　diseases.　There　are　mainly　two　challenges　for　this　task:　first,　the　gene　expression　from　each　disease　may　contain　noise;　second,　the　common　factors　underlying　multiple　diseases　are　hard　to　detect.　To　address　these　challenges,　we　propose　a　novel　deep　architecture　to　discover　the　mutual　functional　gene　clusters　across　multiple　types　of　diseases.　To　demonstrate　that　the　proposed　method　can　discover　precise　and　meaningful　gene　clusters　which　are　not　directly　obtainable　from　traditional　methods,　we　perform　extensive　experimental　studies　on　both　synthetic　and　real　datasets　-　public　gene-expression　data　of　three　types　of　cancers.　Experimental　results　show　that　the　proposed　approach　is　highly　effective　in　discovering　the　mutual　functional　gene　clusters.　©　2013　IEEE.